Data

Tasks The Data Scientist will work within the Global Data & AI organization supporting Daimler Trucks Electrification and Quality Engineering functions. The role focuses on applying machine learning, statistical modeling, and signal analysis to battery data from test benches, field vehicles, telematics systems, and BMS (Battery Management System) logs. The primary objective is to detect early signs of degradation, thermal instability, SOH/SOC inconsistencies, charging anomalies, and performance drifts. The role includes developing classical AI/ML models, conducting feature engineering for time-series sensor data, creating diagnostic dashboards, and contributing to predictive maintenance solutions for battery systems. This position is ideal for candidates with 13 years of experience and a strong interest in automotive electrification and battery analytics. Battery Analytics & Modeling Build classical ML models (Random Forests, SVMs, Gradient Boosting, clustering algorithms) for detecting battery anomalies and predicting degradation. Analyze BMS data such as: Voltage/Current curves Temperature gradients Charging/discharging cycles SOC/SOH trends Cell balancing behavior Develop time-series models for early detection of: Fastrising temperatures Unstable cell voltages Internal resistance changes Charging anomalies & slow charging patterns Data Engineering & Feature Development Perform statistical analysis and feature engineering on battery sensor data. Work with data engineers to ingest and process large telematics/battery datasets. Develop derived KPIs such as capacity fade, thermal runaway indicators, cyclelife predictors, etc. Collaborate on data pipelines using Python, SQL, Azure Data Factory, or similar tools. Model Deployment & Collaboration Support deployment of models into cloud or onprem execution environments. Work with Quality SMEs, Battery System Engineers, and Vehicle Testing teams. Build explainability artifacts and present findings through dashboards (Power BI/Plotly). Research & Optimization Benchmark battery behavior across vehicle models, climatic conditions, and duty cycles. Conduct root-cause analysis using clustering, PCA, or anomaly scoring. Contribute to documentation, model versioning, and continuous improvement processes. Job number: 6302 Publication period: 06/22/2026 - 06/23/2026 Location: Bangalore Organization: Daimler Truck Innovation Center India Private Limited Job Category: Quality Management Working hours: Full time Benefits Good public transport Inhouse Doctor Parking Canteen-Cafeteria Barrier-free workplace To Location: Bengaluru, Daimler Truck Innovation Center India Private Limited Contact Recruiter Name: Sarita Singh Deo Email: sarita.singh_deo@daimlertruck.com Tasks The Data Scientist will work within the Global Data & AI organization supporting Daimler Trucks Electrification and Quality Engineering functions. The role focuses on applying machine learning, statistical modeling, and signal analysis to battery data from test benches, field vehicles, telematics systems, and BMS (Battery Management System) logs. The primary objective is to detect early signs of degradation, thermal instability, SOH/SOC inconsistencies, charging anomalies, and performance drifts. The role includes developing classical AI/ML models, conducting feature engineering for time-series sensor data, creating diagnostic dashboards, and contributing to predictive maintenance solutions for battery systems. This position is ideal for candidates with 13 years of experience and a strong interest in automotive electrification and battery analytics. Battery Analytics & Modeling Build classical ML models (Random Forests, SVMs, Gradient Boosting, clustering algorithms) for detecting battery anomalies and predicting degradation. Analyze BMS data such as: Voltage/Current curves Temperature gradients Charging/discharging cycles SOC/SOH trends Cell balancing behavior Develop time-series models for early detection of: Fastrising temperatures Unstable cell voltages Internal resistance changes Charging anomalies & slow charging patterns Data Engineering & Feature Development Perform statistical analysis and feature engineering on battery sensor data. Work with data engineers to ingest and process large telematics/battery datasets. Develop derived KPIs such as capacity fade, thermal runaway indicators, cyclelife predictors, etc. Collaborate on data pipelines using Python, SQL, Azure Data Factory, or similar tools. Model Deployment & Collaboration Support deployment of models into cloud or onprem execution environments. Work with Quality SMEs, Battery System Engineers, and Vehicle Testing teams. Build explainability artifacts and present findings through dashboards (Power BI/Plotly). Research & Optimization Benchmark battery behavior across vehicle models, climatic conditions, and duty cycles. Conduct root-cause analysis using clustering, PCA, or anomaly scoring. Contribute