Machine Learning Engineer(DK1JO251111E)

We are looking for a Machine Learning Engineer with strong expertise in R&D-driven algorithm development, statistical modeling, and preferably a solid understanding of vehicle (automotive) behavior across both EV (electric vehicles) and ICE (internal combustion engine) powertrains.

The role focuses on designing and building core predictive algorithms that analyze how vehicle usage patterns influence health, efficiency, and cost, translating these insights into actionable intelligence across the OXRED platform.

Algorithm Development (R&D Focus)

• Research, formulate, and develop new algorithms for EV and ICE vehicles across modules such as Battery/Engine Health Models, Predictive Maintenance, Charging Optimization, Financial Forecasting, and more.
• Build physics-informed or hybrid ML models combining statistical, rule-based, and deep learning methods.
• Run controlled experiments, simulations, and ablation studies.

Statistical Modelling & Predictive Analysis

• Develop statistical models to quantify how different vehicle usage patterns affect battery health, engine health, degradation, and energy consumption.
• Fine-tune existing models and develop analysis, including Time-series and Bayesian models for predicting SOH, RUL, maintenance cycles, and failure probabilities.
• Design feature engineering pipelines for large-scale telematics and IoT sensor data.
• Validate results rigorously using statistical tests and real-world dataset benchmarking.

Vehicle Domain Analytics

• Apply EV and ICE domain knowledge to build interpretable, reliable models.
• Understand vehicle operating conditions, charging/usage profiles, degradation behaviors, and maintenance patterns.
• Work closely with EV experts to translate domain insights into model features and algorithmic strategies.

Deployment & Integration

• Implement scalable pipelines using Python, FastAPI, Docker, and cloud-native infrastructure.

• Work with data engineering and platform teams to optimize data flow, feature stores, and model latency.

• Partner with product teams to convert models into production-ready APIs for customers.Qualifications

Technical:

• Strong proficiency in statistical modeling, including regression, time-series, Bayesian models, survival analysis, uncertainty quantification, etc.
• 4-8 years of hands-on experience developing machine learning models in Python, with tools like NumPy, Pandas, Scikit-learn, Statsmodels, PyTorch/TensorFlow.
• Experience designing end-to-end ML pipelines from experimentation to deployment.
• Solid understanding of data preprocessing, missing data handling, sensor signal analysis, and feature engineering.

Preferred Qualifications:

Desirable Skills

• Prior working experience in EV analytics, automotive systems, telematics, or battery/engine modeling.
• Good understanding of EV concepts such as SOH, SOC, degradation, charging patterns, thermal behavior, etc.
• Familiarity with ICE vehicle systems, maintenance cycles, fault patterns, and performance indicators.
• Ability to translate multi-step complex technical solutions into reproducible, documented algorithms.

General Skills

• Excellent problem-solving, documentation, and analytical skills.
• Ability to work in cross-functional teams with product, engineering, and domain experts.
• Comfortable working in a fast-paced, iterative environment.