<span>Battery Data Scientist</span>

Wartsila - Herndon, VA

Full-time - Mid Level

Remote - Herndon, VA

Repair and Maintenance

About the position

The Battery Data Scientist at Wärtsilä Energy Storage & Optimization (ES&O) plays a crucial role in advancing the development of the Battery Intelligence Platform. This position focuses on extracting insights from Energy Management Systems Data, developing algorithms, simulation tools, and forecasting methods for energy systems using Python. The role is integral to the Simulation and Data Science team, contributing to software capabilities and supporting new product development initiatives in a dynamic and fast-paced environment.

Responsibilities

• Track and report ongoing lab tests.
• Build statistical models to predict and track energy storage performance.
• Apply lab insights to field data for diagnostics and recommendations.
• Test and validate models using lab and field data.
• Create cloud-based processes for training and validating models.
• Deploy forecast models for automated results retrieval.
• Turn models into software and test them on historical data.
• Collaborate with cloud services teams to optimize and debug Python solutions in a hybrid environment.
• Optimize models for time series analysis, forecasting, and energy management.
• Develop algorithms for battery performance metrics like State of Charge (SoC) and State of Health (SoH).
• Document new methods and model improvements for clarity and justification.

Requirements

• M.S. or Ph.D. in physics, mathematics, or engineering.
• Research related to and experience with lithium-ion batteries.
• Proficiency in Python as a programming language (2+ years).
• Expertise in statistical analytics including regressions and machine learning techniques.
• Track record of a career trajectory towards and in software engineering.
• Ability to work independently and provide strong leadership.

Nice-to-haves

• Focus on renewable energy in studies or work.
• Experience with large-scale battery power plants.
• Deep knowledge of LFP batteries.
• Skills in predictive analytics and machine learning.
• Background in the energy industry (work or studies).
• Experience running advanced statistical or machine learning algorithms in production.
• Proficiency with open-source analytics tools.
• Familiarity with large, distributed datasets for fast computing.
• Leadership and involvement in renewable energy groups or forums.

Benefits

• Competitive salary
• Comprehensive benefits package
• Personal and professional development opportunities
• Flexible hybrid work model with in-office collaboration