Data Scientist | EMC Portfolio Company

Data Scientist/ Analyst

Data Scientist

If you’re passionate about turning complex data into actionable insights and shaping data-driven products, this is your opportunity to make an impact in a fast-scaling startup environment.

About TrusTerra

TrusTerra is an AI-backed platform designed to transform the used electric vehicle (EV) resale market by tackling its most significant challenges: a lack of trust, standardization, and reliable resale value.

The company's mission is to create a transparent and trustworthy marketplace that benefits both buyers and sellers of pre-owned EVs. As a data Scientist, your role will be key in achieving this objective.

Data Scientist / Analyst

Overall Experience: 5+ Years of experience in Data Science across multiple domains, with a strong preference for the automotive and Electric Vehicles (EV) sector. Proven track record of providing data-

driven solutions using a wide variety of techniques including Deep Learning, Machine Learning, and Natural Language Processing.

Education: Bachelor's degree in Computer Science, IT or a related field. Master's or PhD degree in

Computer Science, Software Engineering, Mathematics, or a related quantitative field.

Technical Skills

• Deep understanding of mathematical concepts (probability, statistics) and extensive experience with

machine learning algorithms and techniques.

• Hands-on experience in data science projects involving pattern recognition, anomaly detection,

predictive/diagnostic analytics, with a specific focus on time-series analysis.

• Proficiency in applying statistical analysis and machine learning algorithms such as Linear Regression, Logistic Regression, Random Forest, Gradient Boosting Trees (XGBoost, LightGBM) etc.

• Proficiency in Python with key libraries: Pandas, scikit-learn, NumPy, SciPy, and familiarity with data

formats like HDF5 (h5py) for large scientific datasets.

• Extensive experience with ML packages/libraries and DL frameworks (TensorFlow, Keras, PyTorch) and computer vision libraries like OpenCV.

• Experience with data visualization tools such as Matplotlib, Seaborn, Plotly, or Tableau/PowerBI to

create compelling dashboards and reports.

Role Description

• Ingest, clean, and preprocess large volumes of structured and unstructured battery data (e.g., voltage, current, temperature, impedance, cycling data) from both laboratory tests and real-world deployments.

• Identify, develop, and select the most informative features and degradation indicators from raw battery time-series data to improve model performance. This includes creating lag features, rolling statistics, and frequency-domain features.

• Design, build, train, and validate predictive ML models (e.g., regression models, neural networks, gradient boosting, and specialized time-series forecasting models like LSTM or Prophet) for State of Health (SoH) estimation and Remaining Useful Life (RUL) prediction.

• Collaborate closely with battery engineers and electrochemists to understand the underlying physical and chemical degradation mechanisms and build more robust, physics-informed models.

• Work with engineering teams to integrate successful models into our analytics platforms or diagnostic devices. This includes monitoring model performance in production (MLOps) and implementing iterative improvements.

• Create clear and compelling visualizations, dashboards, and reports to communicate complex battery degradation trends, model outputs, and actionable insights to both technical and non-technical stakeholders.