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    gscharly/README.md

    Hi 👋 My name is Carlos Gomez

    Data Scientist / ML Engineer | Python, ML | Help businesses make data-driven decisions and build ML based-data products

    • 🌍 I'm based in Madrid
    • ✉️ You can contact me at carlos.gomez.sanchez94@gmail.com
    • 6+ years in developing end-to-end ML data-based products: gathering initial requirements, discussions with business stakeholders, use cases definition, exploratory analysis, ETL pipelines, ML models, evaluation & A/B testing, model monitoring and production
    • Expertise in applying Machine Learning to solving business use cases, from initial prototypes to production ML-based products following MLOps best practices
    • In-depth understanding of Python
    • Experience in Functional and Object-Oriented Programming styles
    • Highly experienced in dealing with high dimension datasets using Spark
    • Knowledge of Cloud platforms, especially Google Cloud Platform
    • Good exposure to CI/CD best practices using Jenkins and GCP
    • Highly sklled in SQL and working with data warehouses
    • Worked for industry clients in the telco and banking sectors

    Skills

    PythonGitDockerTensorFlowPyTorchGoogle Cloud

    Tools | Frameworks

    • Python ML & DL toolkit: Scikit-learn, Lightgbm, Tensorflow (Keras), Pytorch, Spacy
    • Data processing: Numpy, Pandas
    • ETL orchestration: Luigi, Apache Airflow
    • Data visualization: Matplotlib, Seaborn, Plotly
    • Cloud Services: GCP (Vertex AI, Cloud Dataproc, Cloud Build, BigQuery), AWS (Sagemaker, EMR)
    • Docker
    • IDEs: Jupyter-lab, Pycharm, IntelliJ
    • Web development with Nodejs and Flask

    Research thesis

    • Automatic generation of sport news using NLP techniques and ranking systems (Universidad Rey Juan Carlos Madrid, 2021)
    • Design and implementation of initialization techniques for Deep Learning algorithms (Universidad Politécnica de Madrid, 2018)

    Socials

    Pinned Loading

    1. drl_p3_collaboration_competitiondrl_p3_collaboration_competitionPublic

      Multi-agent Reinforcement Learning - Train a pair of agents to play tennis. Part of Udacity DRL Nanodegree

      Python

    2. drl_p2_continous_controldrl_p2_continous_controlPublic

      Policy Based Methods - Train a double-jointed arm to follow target locations. Part of Udacity DRL Nanodegree

      Python

    3. drl_p1_navigationdrl_p1_navigationPublic

      Value Based Methods - Train an agent to collect bananas. Part of Udacity DRL Nanodegree

      Python

    4. master_ds_tfmmaster_ds_tfmPublic

      Data Science Master degree Final thesis. Automatic generation of sport news using NLP and ranking techniques

      Jupyter Notebook

    5. practica_sistemas_distribuidospractica_sistemas_distribuidosPublic

      Sentiment analysis in Spain using Twitter and MRJob. Part of Master Degree on Data Science in URJC Madrid

      Python

    6. chefs_graphchefs_graphPublic

      This project uses graph analysis techniques to visualize the relation between different chefs

      HTML

    , 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
     blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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      Achievement: Arctic Code Vault Contributor
      Highlights
      • 
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      gscharly/README.md
      Hi 👋 My name is Carlos Gomez
      Data Scientist / ML Engineer | Python, ML | Help businesses make data-driven decisions and build ML based-data products
      • 🌍 I'm based in Madrid
      • ✉️ You can contact me at carlos.gomez.sanchez94@gmail.com
      • 6+ years in developing end-to-end ML data-based products: gathering initial requirements, discussions with business stakeholders, use cases definition, exploratory analysis, ETL pipelines, ML models, evaluation & A/B testing, model monitoring and production
      • Expertise in applying Machine Learning to solving business use cases, from initial prototypes to production ML-based products following MLOps best practices
      • In-depth understanding of Python
      • Experience in Functional and Object-Oriented Programming styles
      • Highly experienced in dealing with high dimension datasets using Spark
      • Knowledge of Cloud platforms, especially Google Cloud Platform
      • Good exposure to CI/CD best practices using Jenkins and GCP
      • Highly sklled in SQL and working with data warehouses
      • Worked for industry clients in the telco and banking sectors
      Skills
      PythonGitDockerTensorFlowPyTorchGoogle Cloud
      Tools | Frameworks
      • Python ML & DL toolkit: Scikit-learn, Lightgbm, Tensorflow (Keras), Pytorch, Spacy
      • Data processing: Numpy, Pandas
      • ETL orchestration: Luigi, Apache Airflow
      • Data visualization: Matplotlib, Seaborn, Plotly
      • Cloud Services: GCP (Vertex AI, Cloud Dataproc, Cloud Build, BigQuery), AWS (Sagemaker, EMR)
      • Docker
      • IDEs: Jupyter-lab, Pycharm, IntelliJ
      • Web development with Nodejs and Flask
      Research thesis
      • Automatic generation of sport news using NLP techniques and ranking systems (Universidad Rey Juan Carlos Madrid, 2021)
      • Design and implementation of initialization techniques for Deep Learning algorithms (Universidad Politécnica de Madrid, 2018)
      Socials
      
Pinned
Loading
      1. drl_p3_collaboration_competitiondrl_p3_collaboration_competitionPublic
        
Multi-agent Reinforcement Learning - Train a pair of agents to play tennis. Part of Udacity DRL Nanodegree

        Python
      2. drl_p2_continous_controldrl_p2_continous_controlPublic
        
Policy Based Methods - Train a double-jointed arm to follow target locations. Part of Udacity DRL Nanodegree

        Python
      3. drl_p1_navigationdrl_p1_navigationPublic
        
Value Based Methods - Train an agent to collect bananas. Part of Udacity DRL Nanodegree

        Python
      4. master_ds_tfmmaster_ds_tfmPublic
        
Data Science Master degree Final thesis. Automatic generation of sport news using NLP and ranking techniques

        Jupyter Notebook
      5. practica_sistemas_distribuidospractica_sistemas_distribuidosPublic
        
Sentiment analysis in Spain using Twitter and MRJob. Part of Master Degree on Data Science in URJC Madrid

        Python
      6. chefs_graphchefs_graphPublic
        
This project uses graph analysis techniques to visualize the relation between different chefs

        HTML
      
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

      Skip to content
      View gscharly's full-sized avatar
      5
followers
 · 1
following

        Achievements
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        Achievements
        Achievement: Arctic Code Vault Contributor
        Highlights
        • 
Pro

        
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        gscharly/README.md
        Hi 👋 My name is Carlos Gomez
        Data Scientist / ML Engineer | Python, ML | Help businesses make data-driven decisions and build ML based-data products
        • 🌍 I'm based in Madrid
        • ✉️ You can contact me at carlos.gomez.sanchez94@gmail.com
        • 6+ years in developing end-to-end ML data-based products: gathering initial requirements, discussions with business stakeholders, use cases definition, exploratory analysis, ETL pipelines, ML models, evaluation & A/B testing, model monitoring and production
        • Expertise in applying Machine Learning to solving business use cases, from initial prototypes to production ML-based products following MLOps best practices
        • In-depth understanding of Python
        • Experience in Functional and Object-Oriented Programming styles
        • Highly experienced in dealing with high dimension datasets using Spark
        • Knowledge of Cloud platforms, especially Google Cloud Platform
        • Good exposure to CI/CD best practices using Jenkins and GCP
        • Highly sklled in SQL and working with data warehouses
        • Worked for industry clients in the telco and banking sectors
        Skills
        PythonGitDockerTensorFlowPyTorchGoogle Cloud
        Tools | Frameworks
        • Python ML & DL toolkit: Scikit-learn, Lightgbm, Tensorflow (Keras), Pytorch, Spacy
        • Data processing: Numpy, Pandas
        • ETL orchestration: Luigi, Apache Airflow
        • Data visualization: Matplotlib, Seaborn, Plotly
        • Cloud Services: GCP (Vertex AI, Cloud Dataproc, Cloud Build, BigQuery), AWS (Sagemaker, EMR)
        • Docker
        • IDEs: Jupyter-lab, Pycharm, IntelliJ
        • Web development with Nodejs and Flask
        Research thesis
        • Automatic generation of sport news using NLP techniques and ranking systems (Universidad Rey Juan Carlos Madrid, 2021)
        • Design and implementation of initialization techniques for Deep Learning algorithms (Universidad Politécnica de Madrid, 2018)
        Socials
        
Pinned
Loading
        1. drl_p3_collaboration_competitiondrl_p3_collaboration_competitionPublic
          
Multi-agent Reinforcement Learning - Train a pair of agents to play tennis. Part of Udacity DRL Nanodegree

          Python
        2. drl_p2_continous_controldrl_p2_continous_controlPublic
          
Policy Based Methods - Train a double-jointed arm to follow target locations. Part of Udacity DRL Nanodegree

          Python
        3. drl_p1_navigationdrl_p1_navigationPublic
          
Value Based Methods - Train an agent to collect bananas. Part of Udacity DRL Nanodegree

          Python
        4. master_ds_tfmmaster_ds_tfmPublic
          
Data Science Master degree Final thesis. Automatic generation of sport news using NLP and ranking techniques

          Jupyter Notebook
        5. practica_sistemas_distribuidospractica_sistemas_distribuidosPublic
          
Sentiment analysis in Spain using Twitter and MRJob. Part of Master Degree on Data Science in URJC Madrid

          Python
        6. chefs_graphchefs_graphPublic
          
This project uses graph analysis techniques to visualize the relation between different chefs

          HTML
        
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

        Skip to content
        View gscharly's full-sized avatar
        5
followers
 · 1
following

          Achievements
          Achievement: Arctic Code Vault Contributor
          Achievements
          Achievement: Arctic Code Vault Contributor
          Highlights
          • 
Pro

          
Block or report gscharly

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          gscharly/README.md
          Hi 👋 My name is Carlos Gomez
          Data Scientist / ML Engineer | Python, ML | Help businesses make data-driven decisions and build ML based-data products
          • 🌍 I'm based in Madrid
          • ✉️ You can contact me at carlos.gomez.sanchez94@gmail.com
          • 6+ years in developing end-to-end ML data-based products: gathering initial requirements, discussions with business stakeholders, use cases definition, exploratory analysis, ETL pipelines, ML models, evaluation & A/B testing, model monitoring and production
          • Expertise in applying Machine Learning to solving business use cases, from initial prototypes to production ML-based products following MLOps best practices
          • In-depth understanding of Python
          • Experience in Functional and Object-Oriented Programming styles
          • Highly experienced in dealing with high dimension datasets using Spark
          • Knowledge of Cloud platforms, especially Google Cloud Platform
          • Good exposure to CI/CD best practices using Jenkins and GCP
          • Highly sklled in SQL and working with data warehouses
          • Worked for industry clients in the telco and banking sectors
          Skills
          PythonGitDockerTensorFlowPyTorchGoogle Cloud
          Tools | Frameworks
          • Python ML & DL toolkit: Scikit-learn, Lightgbm, Tensorflow (Keras), Pytorch, Spacy
          • Data processing: Numpy, Pandas
          • ETL orchestration: Luigi, Apache Airflow
          • Data visualization: Matplotlib, Seaborn, Plotly
          • Cloud Services: GCP (Vertex AI, Cloud Dataproc, Cloud Build, BigQuery), AWS (Sagemaker, EMR)
          • Docker
          • IDEs: Jupyter-lab, Pycharm, IntelliJ
          • Web development with Nodejs and Flask
          Research thesis
          • Automatic generation of sport news using NLP techniques and ranking systems (Universidad Rey Juan Carlos Madrid, 2021)
          • Design and implementation of initialization techniques for Deep Learning algorithms (Universidad Politécnica de Madrid, 2018)
          Socials
          
Pinned
Loading
          1. drl_p3_collaboration_competitiondrl_p3_collaboration_competitionPublic
            
Multi-agent Reinforcement Learning - Train a pair of agents to play tennis. Part of Udacity DRL Nanodegree

            Python
          2. drl_p2_continous_controldrl_p2_continous_controlPublic
            
Policy Based Methods - Train a double-jointed arm to follow target locations. Part of Udacity DRL Nanodegree

            Python
          3. drl_p1_navigationdrl_p1_navigationPublic
            
Value Based Methods - Train an agent to collect bananas. Part of Udacity DRL Nanodegree

            Python
          4. master_ds_tfmmaster_ds_tfmPublic
            
Data Science Master degree Final thesis. Automatic generation of sport news using NLP and ranking techniques

            Jupyter Notebook
          5. practica_sistemas_distribuidospractica_sistemas_distribuidosPublic
            
Sentiment analysis in Spain using Twitter and MRJob. Part of Master Degree on Data Science in URJC Madrid

            Python
          6. chefs_graphchefs_graphPublic
            
This project uses graph analysis techniques to visualize the relation between different chefs

            HTML
          
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

          Skip to content
          View gscharly's full-sized avatar
          5
followers
 · 1
following

            Achievements
            Achievement: Arctic Code Vault Contributor
            Achievements
            Achievement: Arctic Code Vault Contributor
            Highlights
            • 
Pro

            
Block or report gscharly

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            gscharly/README.md
            Hi 👋 My name is Carlos Gomez
            Data Scientist / ML Engineer | Python, ML | Help businesses make data-driven decisions and build ML based-data products
            • 🌍 I'm based in Madrid
            • ✉️ You can contact me at carlos.gomez.sanchez94@gmail.com
            • 6+ years in developing end-to-end ML data-based products: gathering initial requirements, discussions with business stakeholders, use cases definition, exploratory analysis, ETL pipelines, ML models, evaluation & A/B testing, model monitoring and production
            • Expertise in applying Machine Learning to solving business use cases, from initial prototypes to production ML-based products following MLOps best practices
            • In-depth understanding of Python
            • Experience in Functional and Object-Oriented Programming styles
            • Highly experienced in dealing with high dimension datasets using Spark
            • Knowledge of Cloud platforms, especially Google Cloud Platform
            • Good exposure to CI/CD best practices using Jenkins and GCP
            • Highly sklled in SQL and working with data warehouses
            • Worked for industry clients in the telco and banking sectors
            Skills
            PythonGitDockerTensorFlowPyTorchGoogle Cloud
            Tools | Frameworks
            • Python ML & DL toolkit: Scikit-learn, Lightgbm, Tensorflow (Keras), Pytorch, Spacy
            • Data processing: Numpy, Pandas
            • ETL orchestration: Luigi, Apache Airflow
            • Data visualization: Matplotlib, Seaborn, Plotly
            • Cloud Services: GCP (Vertex AI, Cloud Dataproc, Cloud Build, BigQuery), AWS (Sagemaker, EMR)
            • Docker
            • IDEs: Jupyter-lab, Pycharm, IntelliJ
            • Web development with Nodejs and Flask
            Research thesis
            • Automatic generation of sport news using NLP techniques and ranking systems (Universidad Rey Juan Carlos Madrid, 2021)
            • Design and implementation of initialization techniques for Deep Learning algorithms (Universidad Politécnica de Madrid, 2018)
            Socials
            
Pinned
Loading
            1. drl_p3_collaboration_competitiondrl_p3_collaboration_competitionPublic
              
Multi-agent Reinforcement Learning - Train a pair of agents to play tennis. Part of Udacity DRL Nanodegree

              Python
            2. drl_p2_continous_controldrl_p2_continous_controlPublic
              
Policy Based Methods - Train a double-jointed arm to follow target locations. Part of Udacity DRL Nanodegree

              Python
            3. drl_p1_navigationdrl_p1_navigationPublic
              
Value Based Methods - Train an agent to collect bananas. Part of Udacity DRL Nanodegree

              Python
            4. master_ds_tfmmaster_ds_tfmPublic
              
Data Science Master degree Final thesis. Automatic generation of sport news using NLP and ranking techniques

              Jupyter Notebook
            5. practica_sistemas_distribuidospractica_sistemas_distribuidosPublic
              
Sentiment analysis in Spain using Twitter and MRJob. Part of Master Degree on Data Science in URJC Madrid

              Python
            6. chefs_graphchefs_graphPublic
              
This project uses graph analysis techniques to visualize the relation between different chefs

              HTML
            
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

            Skip to content
            View gscharly's full-sized avatar
            5
followers
 · 1
following

              Achievements
              Achievement: Arctic Code Vault Contributor
              Achievements
              Achievement: Arctic Code Vault Contributor
              Highlights
              • 
Pro

              
Block or report gscharly

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              Report abuse
              gscharly/README.md
              Hi 👋 My name is Carlos Gomez
              Data Scientist / ML Engineer | Python, ML | Help businesses make data-driven decisions and build ML based-data products
              • 🌍 I'm based in Madrid
              • ✉️ You can contact me at carlos.gomez.sanchez94@gmail.com
              • 6+ years in developing end-to-end ML data-based products: gathering initial requirements, discussions with business stakeholders, use cases definition, exploratory analysis, ETL pipelines, ML models, evaluation & A/B testing, model monitoring and production
              • Expertise in applying Machine Learning to solving business use cases, from initial prototypes to production ML-based products following MLOps best practices
              • In-depth understanding of Python
              • Experience in Functional and Object-Oriented Programming styles
              • Highly experienced in dealing with high dimension datasets using Spark
              • Knowledge of Cloud platforms, especially Google Cloud Platform
              • Good exposure to CI/CD best practices using Jenkins and GCP
              • Highly sklled in SQL and working with data warehouses
              • Worked for industry clients in the telco and banking sectors
              Skills
              PythonGitDockerTensorFlowPyTorchGoogle Cloud
              Tools | Frameworks
              • Python ML & DL toolkit: Scikit-learn, Lightgbm, Tensorflow (Keras), Pytorch, Spacy
              • Data processing: Numpy, Pandas
              • ETL orchestration: Luigi, Apache Airflow
              • Data visualization: Matplotlib, Seaborn, Plotly
              • Cloud Services: GCP (Vertex AI, Cloud Dataproc, Cloud Build, BigQuery), AWS (Sagemaker, EMR)
              • Docker
              • IDEs: Jupyter-lab, Pycharm, IntelliJ
              • Web development with Nodejs and Flask
              Research thesis
              • Automatic generation of sport news using NLP techniques and ranking systems (Universidad Rey Juan Carlos Madrid, 2021)
              • Design and implementation of initialization techniques for Deep Learning algorithms (Universidad Politécnica de Madrid, 2018)
              Socials
              
Pinned
Loading
              1. drl_p3_collaboration_competitiondrl_p3_collaboration_competitionPublic
                
Multi-agent Reinforcement Learning - Train a pair of agents to play tennis. Part of Udacity DRL Nanodegree

                Python
              2. drl_p2_continous_controldrl_p2_continous_controlPublic
                
Policy Based Methods - Train a double-jointed arm to follow target locations. Part of Udacity DRL Nanodegree

                Python
              3. drl_p1_navigationdrl_p1_navigationPublic
                
Value Based Methods - Train an agent to collect bananas. Part of Udacity DRL Nanodegree

                Python
              4. master_ds_tfmmaster_ds_tfmPublic
                
Data Science Master degree Final thesis. Automatic generation of sport news using NLP and ranking techniques

                Jupyter Notebook
              5. practica_sistemas_distribuidospractica_sistemas_distribuidosPublic
                
Sentiment analysis in Spain using Twitter and MRJob. Part of Master Degree on Data Science in URJC Madrid

                Python
              6. chefs_graphchefs_graphPublic
                
This project uses graph analysis techniques to visualize the relation between different chefs

                HTML
              
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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following

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                Achievement: Arctic Code Vault Contributor
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                gscharly/README.md
                Hi 👋 My name is Carlos Gomez
                Data Scientist / ML Engineer | Python, ML | Help businesses make data-driven decisions and build ML based-data products
                • 🌍 I'm based in Madrid
                • ✉️ You can contact me at carlos.gomez.sanchez94@gmail.com
                • 6+ years in developing end-to-end ML data-based products: gathering initial requirements, discussions with business stakeholders, use cases definition, exploratory analysis, ETL pipelines, ML models, evaluation & A/B testing, model monitoring and production
                • Expertise in applying Machine Learning to solving business use cases, from initial prototypes to production ML-based products following MLOps best practices
                • In-depth understanding of Python
                • Experience in Functional and Object-Oriented Programming styles
                • Highly experienced in dealing with high dimension datasets using Spark
                • Knowledge of Cloud platforms, especially Google Cloud Platform
                • Good exposure to CI/CD best practices using Jenkins and GCP
                • Highly sklled in SQL and working with data warehouses
                • Worked for industry clients in the telco and banking sectors
                Skills
                PythonGitDockerTensorFlowPyTorchGoogle Cloud
                Tools | Frameworks
                • Python ML & DL toolkit: Scikit-learn, Lightgbm, Tensorflow (Keras), Pytorch, Spacy
                • Data processing: Numpy, Pandas
                • ETL orchestration: Luigi, Apache Airflow
                • Data visualization: Matplotlib, Seaborn, Plotly
                • Cloud Services: GCP (Vertex AI, Cloud Dataproc, Cloud Build, BigQuery), AWS (Sagemaker, EMR)
                • Docker
                • IDEs: Jupyter-lab, Pycharm, IntelliJ
                • Web development with Nodejs and Flask
                Research thesis
                • Automatic generation of sport news using NLP techniques and ranking systems (Universidad Rey Juan Carlos Madrid, 2021)
                • Design and implementation of initialization techniques for Deep Learning algorithms (Universidad Politécnica de Madrid, 2018)
                Socials
                
Pinned
Loading
                1. drl_p3_collaboration_competitiondrl_p3_collaboration_competitionPublic
                  
Multi-agent Reinforcement Learning - Train a pair of agents to play tennis. Part of Udacity DRL Nanodegree

                  Python
                2. drl_p2_continous_controldrl_p2_continous_controlPublic
                  
Policy Based Methods - Train a double-jointed arm to follow target locations. Part of Udacity DRL Nanodegree

                  Python
                3. drl_p1_navigationdrl_p1_navigationPublic
                  
Value Based Methods - Train an agent to collect bananas. Part of Udacity DRL Nanodegree

                  Python
                4. master_ds_tfmmaster_ds_tfmPublic
                  
Data Science Master degree Final thesis. Automatic generation of sport news using NLP and ranking techniques

                  Jupyter Notebook
                5. practica_sistemas_distribuidospractica_sistemas_distribuidosPublic
                  
Sentiment analysis in Spain using Twitter and MRJob. Part of Master Degree on Data Science in URJC Madrid

                  Python
                6. chefs_graphchefs_graphPublic
                  
This project uses graph analysis techniques to visualize the relation between different chefs

                  HTML
                
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

                Skip to content
                View gscharly's full-sized avatar
                5
followers
 · 1
following

                  Achievements
                  Achievement: Arctic Code Vault Contributor
                  Achievements
                  Achievement: Arctic Code Vault Contributor
                  Highlights
                  • 
Pro

                  
Block or report gscharly

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                  gscharly/README.md
                  Hi 👋 My name is Carlos Gomez
                  Data Scientist / ML Engineer | Python, ML | Help businesses make data-driven decisions and build ML based-data products
                  • 🌍 I'm based in Madrid
                  • ✉️ You can contact me at carlos.gomez.sanchez94@gmail.com
                  • 6+ years in developing end-to-end ML data-based products: gathering initial requirements, discussions with business stakeholders, use cases definition, exploratory analysis, ETL pipelines, ML models, evaluation & A/B testing, model monitoring and production
                  • Expertise in applying Machine Learning to solving business use cases, from initial prototypes to production ML-based products following MLOps best practices
                  • In-depth understanding of Python
                  • Experience in Functional and Object-Oriented Programming styles
                  • Highly experienced in dealing with high dimension datasets using Spark
                  • Knowledge of Cloud platforms, especially Google Cloud Platform
                  • Good exposure to CI/CD best practices using Jenkins and GCP
                  • Highly sklled in SQL and working with data warehouses
                  • Worked for industry clients in the telco and banking sectors
                  Skills
                  PythonGitDockerTensorFlowPyTorchGoogle Cloud
                  Tools | Frameworks
                  • Python ML & DL toolkit: Scikit-learn, Lightgbm, Tensorflow (Keras), Pytorch, Spacy
                  • Data processing: Numpy, Pandas
                  • ETL orchestration: Luigi, Apache Airflow
                  • Data visualization: Matplotlib, Seaborn, Plotly
                  • Cloud Services: GCP (Vertex AI, Cloud Dataproc, Cloud Build, BigQuery), AWS (Sagemaker, EMR)
                  • Docker
                  • IDEs: Jupyter-lab, Pycharm, IntelliJ
                  • Web development with Nodejs and Flask
                  Research thesis
                  • Automatic generation of sport news using NLP techniques and ranking systems (Universidad Rey Juan Carlos Madrid, 2021)
                  • Design and implementation of initialization techniques for Deep Learning algorithms (Universidad Politécnica de Madrid, 2018)
                  Socials
                  
Pinned
Loading
                  1. drl_p3_collaboration_competitiondrl_p3_collaboration_competitionPublic
                    
Multi-agent Reinforcement Learning - Train a pair of agents to play tennis. Part of Udacity DRL Nanodegree

                    Python
                  2. drl_p2_continous_controldrl_p2_continous_controlPublic
                    
Policy Based Methods - Train a double-jointed arm to follow target locations. Part of Udacity DRL Nanodegree

                    Python
                  3. drl_p1_navigationdrl_p1_navigationPublic
                    
Value Based Methods - Train an agent to collect bananas. Part of Udacity DRL Nanodegree

                    Python
                  4. master_ds_tfmmaster_ds_tfmPublic
                    
Data Science Master degree Final thesis. Automatic generation of sport news using NLP and ranking techniques

                    Jupyter Notebook
                  5. practica_sistemas_distribuidospractica_sistemas_distribuidosPublic
                    
Sentiment analysis in Spain using Twitter and MRJob. Part of Master Degree on Data Science in URJC Madrid

                    Python
                  6. chefs_graphchefs_graphPublic
                    
This project uses graph analysis techniques to visualize the relation between different chefs

                    HTML