Data Scientist
Role details
Job location
Tech stack
Job description
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Develop and deploy AI-based solutions for business challenges.
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Collaborate with teams to align data strategies with business goals.
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Manage the full lifecycle of AI models ensuring scalability., As a Senior Data Scientist, you will develop AI and machine learning models to deliver data-driven solutions that help EDP CS stay ahead in the competitive energy market. You will collaborate with cross-functional teams to ensure the scalability and impact of these solutions across international markets, using your expertise in GPT, Azure, MLOps, and Databricks. Main accountabilities
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Identify, design, develop, and deploy AI-based solutions using various techniques to address business challenges and drive innovation in commercial energy solutions.
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Analyze and understand business needs, collaborating with cross-functional teams to align data strategies with business objectives.
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Optimize processes and enhance customer experiences.
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Manage the full lifecycle of AI models, ensuring scalability and integration with platforms like Azure and Databricks to support EDP CS' strategic goals.
Requirements
The ideal candidate should have a strong background in mathematics and at least 3 years of experience in Data Science. Proficiency in programming languages like Python and knowledge of Azure are essential., * Minimum 3 to 6 years in Data Science or Machine Learning Engineering.
- Proven track record in developing advanced machine learning models.
- Experience with GPT or natural language processing technologies., * Educational background: Bachelor's degree in Computer Science, Mathematics, Statistics, Data Science, Artificial Intelligence, or a related field.
- Master's or PhD in Data Science, Machine Learning, or Software Engineering is highly valued.
- Strong mathematical foundation: Solid understanding of linear algebra, statistics, calculus, and optimization applied to predictive modeling and advanced data analysis.
Language
- Spanish.
- Excellent written and spoken English.
- Additional languages are a plus.
Professional experience
- Minimum 3 to 6 years in roles related to Data Science or Machine Learning Engineering.
- Proven track record in developing and deploying advanced machine learning and deep learning models in production environments.
- Hands-on experience in designing and implementing solutions using GPT or natural language processing technologies.
- Demonstrated expertise in MLOps: building pipelines, implementing continuous integration, and automating processes for model training and deployment.
- Extensive use of Azure Machine Learning, Databricks, or similar cloud-based solutions.
- Active involvement in collaborative projects with cross-functional teams, including engineers and business stakeholders.
Knowledge: Tools and technologies
- Programming languages: Python, R; frameworks such as TensorFlow, Keras, PyTorch, Hugging Face, Shiny.
- Familiarity with OpenAI models such as Whisper, GPT, Codex.
- Cloud platforms: advanced knowledge of Azure (Azure ML, Azure Data Factory, Azure Synapse), valuable AWS or GCP.
- Data infrastructure: expertise with Databricks, Spark, and relational/non-relational databases.
- Experience with foundational models and generative AI tools like OpenAI GPT, ChatGPT and their integrations.
- MLOps tools: MLflow, DVC, orchestrators such as Airflow or Kubeflow.
- Data management: data cleaning, transformation, and preparation using Pandas, PySpark, or SQL.
- Good security practices in code (using key vaults and protecting sensitive variables such as API keys).
Additional knowledge (optional)
- Advanced concepts in data security and privacy (e.g., GDPR).
- API development for integrating models into production systems.
- Data visualization tools like Power BI, Tableau, DOMO or libraries like Matplotlib and Seaborn., * Critical thinking and problem-solving: ability to tackle complex technical challenges and propose innovative data-driven solutions.
- Teamwork: excellent communication skills to collaborate effectively with technical and business stakeholders.