Lead Data Scientist

Description

• Establish clear validation frameworks to ensure models meet required performance standards and business objectives.
• Implement automated testing strategies and pipeline to streamline model validation processes.
• 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.
• Collaborate with Data Engineers and ML Engineers to improve data quality, enhance model performance, and ensure efficient deployment of validated models.
• Ensure that validation processes adhere to data governance policies and industry standards.
• Design and conduct experiments to test and validate AI/ML models in the context of energy systems and grid automation applications.
• Analyze model performance against real-world data to ensure accuracy, reliability, and scalability.
• Communicate validation results, insights, and recommendations clearly to stakeholders, including product managers and leadership teams.

Requirements

• Strong communication skills, with the ability to clearly explain complex validation results to non-technical stakeholders.
• Understanding of distributed computing environments and large-scale model deployment.
• Master’s, or Bachelor’s degree in Data Science, Computer Science, Electrical Engineering, or a related field, with hands-on experience in model validation.
• Strong knowledge of statistical techniques, model performance metrics, and AI/ML validation methodologies.
• Experience typically gained over +5 years in large multinational companies within the energy sector or related industrial domains such as smart infrastructure or industrial automation.
• Experience with data visualization tools (e.g., Tableau, Power BI) to effectively present validation results and insights.
• Experience with data wrangling, feature engineering, and dataset preparation for model validation.
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and deploying models in cloud environments.
• Familiarity with big data tools and technologies such as Hadoop, Kafka, and Spark.
• Knowledge of data governance frameworks and validation standards in the energy sector.
• Solid experience in validating AI/ML models, ensuring they meet business and technical requirements.
• Proficiency in programming languages such as Python, R, or MATLAB.
• Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and model evaluation techniques.

Benefits

Training + Development

Interview process

Visa Sponsorship

Security clearance