Data Scientist

We are looking for a skilled Data Scientist to develop advanced battery health algorithms powered by real-time vehicle data using state-of-the-art statistical and machine learning techniques.

Key Responsibilities:

• Develop data analysis pipelines to interpret vehicle telemetry and battery usage patterns across various vehicle applications.

• Design, build, validate, and maintain predictive models that deliver battery health insights for connected battery solutions.

• Analyze machine and production-line data to better understand manufacturing processes and operational performance.

• Develop and maintain ML/statistical models aimed at:

• improving production throughput,

• reducing scrap rates,

• enhancing product quality,

• and optimizing manufacturing efficiency.

• Collaborate closely with data scientists, engineers, and business stakeholders to design effective, data-driven solutions.

• Communicate analytical findings and decision-making processes to both technical and non-technical audiences.

• Support cross-functional teams across the organization with machine learning and statistical modeling expertise.

Required Qualifications:

• Bachelor's degree in Statistics, Mathematics, Computer Science, Engineering, or a related technical field.

• 3+ years of professional experience in data science, machine learning, or applied statistics, or equivalent academic research experience through a Master's or PhD program.

• Strong programming skills in Python, Julia, or R, including experience with ML/statistical libraries such as:

• Scikit-learn,

• SciPy,

• Statsmodels,

• PyTorch,

• and Keras.

• Experience using BI and visualization tools such as Power BI or Tableau.

• Strong knowledge of machine learning methods including:

• supervised and unsupervised learning,

• feature selection,

• dimensionality reduction,

• regression,

• classification,

• clustering,

• and time-series analysis.

• Experience working with databases and writing SQL queries.

• Hands-on experience developing, training, validating, and deploying ML/statistical models.