Lead Data Scientist

Summary

Are you excited at the opportunity to electrify and decarbonize the world?We are looking for a passionate, creative, and results-driven Data Scientist with solid experience in developing and validating AI/ML models, typically gained through at least 5 years working within the energy sector or related domains such as smart infrastructure or industrial automation.

The ideal candidate has a strong track record of independently leading and delivering AI/ML model projects in complex, data-rich environments, requiring someone who can drive innovation from concept to production.As part of the Grid Automation organization, you will be at the forefront of developing, testing, and validating cutting-edge AI/ML models specifically designed for grid innovation applications. You will play a key role in designing and building robust validation frameworks that ensure AI/ML solutions meet stringent accuracy, performance, and operational standards across both edge and cloud environments.

Job Description

Essential Responsibilities:

• Design and conduct experiments to develop, test and validate AI/ML models in the context of energy systems and grid automation applications (including model selection, design, tuning, testing, refining, validation, optimization and deployment).
• Establish clear validation frameworks to ensure models meet required performance standards and business objectives.
• Establish test procedures to validate models with real and simulated grid data.
• Identify and address discrepancies between expected and actual model behavior, providing actionable insights to improve model performance.
• Implement automated testing strategies and pipelines to streamline model validation processes.
• Responsible for exploratory data analysis (EDA), reporting and visualization of results.
• Lead project stakeholder workshops to shape scope, designs and outcomes to ensure project vision becomes reality.
• Collaborate with Application Architects, Data Engineers, ML Engineers and MLOps Engineers to improve data quality, enhance model performance, and ensure efficient deployment of validated models.
• Ensure data adheres to data governance policies and industry standards.
• Communicate validation results, insights, and recommendations clearly to stakeholders, including product managers and leadership teams.
• Build necessary understanding and expertise to design and develop product features and applications such as Protection, Control, Monitoring and Communication

Must-Have Requirements

• PhD, Masters, or Bachelors degree in Data Science, Computer Science, Electrical Engineering, or a related field with hands-on experience in model development and validation.
• Proven experience in the energy, smart infrastructure, or industrial automation sectors, with deep expertise in system protection, automation, monitoring, and diagnostics, typically acquired through a minimum of 5 years within a multinational manufacturing company.
• Substantial experience in building and validating AI/ML models, ensuring they meet business and technical requirements.
• Strong knowledge of statistical techniques, model technologies, performance metrics, and validation methodologies for AI/ML models.
• Proficiency in programming languages such as Python, R, or MATLAB.
• Experience with data wrangling, feature engineering, and preparing datasets for model development.
• Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and model evaluation techniques.
• Experience in exploratory data analysis (EDA), delivering actionable insights from data and data storytelling.
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and deployment of models in cloud environments.
• Proficiency with data visualization tools and libraries such as Tableau, Power BI, or similar (matplotlib, bokeh, seaborn) to effectively present results and insights.

Nice-to-Have Requirements:

• Familiarity with big data tools and technologies, such as Hadoop, Kafka, and Spark.
• Familiarity with data governance frameworks and validation standards in the energy sector.
• Knowledge of distributed computing environments and model deployment at scale.
• Strong communication skills, with the ability to clearly explain complex validation results to non-technical stakeholders.