Data Scientist

Responsibilities & Key Deliverables

Role Summary

We are looking for a hands-on, algorithm-strong ML/DL leader who can own and drive high-impact AI initiatives in Product Development (PD)—from data strategy to model development, validation, and deployment. The role demands deep expertise in core machine learning and deep learning fundamentals, and strong experience with time-series sensor data and signal processing (preferred), to build robust, production-grade models that deliver measurable reductions in engineering cycle time and automation of PD workflows.

This is not a reporting-only analytics role—you will lead initiative delivery, mentor engineers, and build scalable ML systems end-to-end.

What You’ll Drive

You will contribute to and lead high-impact AI initiatives that transform the automotive product development process and materially accelerate program timelines. You will work on complex, challenging, and highly rewarding problems that redefine the way vehicle platforms are engineered, simulated, and tested—enabling faster decisions, improved quality, and scalable AI-driven execution across PD.

Key Responsibilities

1) Algorithmic Model Ownership (Core ML/DL)
• Design and implement ML/DL architectures for structured and time-series data: gradient boosting, deep sequence models, representation learning, multi-modal learning, uncertainty estimation, and robust generalization.
• Apply rigorous model evaluation, error analysis, and experimental design, especially for peak/rare-event behavior common in physical systems.
• Build interpretable and physics-aware ML where needed (feature constraints, monotonicity, hybrid models).

2) Time Series & Signal Processing (Preferred)
• Own sensor data pipelines: synchronization, resampling, windowing, denoising, drift handling, filtering, and feature extraction.
• Apply signal processing methods (e.g., FFT/spectral features, band-pass/high-pass/low-pass filtering, wavelets) where beneficial.

3) End-to-End Delivery & Productionization
• Build and maintain end-to-end ML pipelines in Python.
• Deploy models using pragmatic engineering stacks (e.g., Docker, FastAPI, MLflow).
• Enable monitoring, retraining, and governance for model performance over time.

Experience

3-5 Years of relevant experience

• Strong experience with time-series sensor data (automotive/IoT/industrial/telemetry), including sequence modeling and feature extraction.
• Signal processing background (filtering intuition, spectral methods, noise/drift handling).
• Familiarity with cloud platforms (AWS/GCP/Azure) and MLOps practices.
• Exposure to engineering/PD domains such as validation, simulation acceleration, or calibration optimization.

Qualifications

B.Tech / M.Tech in relevant domain

General Requirements

Core ML/DL Depth
• Strong fundamentals in bias–variance, regularization, optimization, generalization, calibration, and leakage avoidance.
• Proficiency with tree-based ML (XGBoost/CatBoost) and deep learning (CNN/RNN/Transformers), including loss functions and training dynamics.
• Feature engineering for physical/time-series systems; robust evaluation and cross-domain transfer.

Strong Coding & Practical ML Engineering
• Advanced Python for ML systems and data workflows.
• Experience with: scikit-learn, XGBoost/CatBoost, PyTorch/TensorFlow.
• Strong data stack: NumPy/Pandas; clear visualization and communication.

Delivery Mindset
• Demonstrated ability to own outcomes, lead projects independently, and ship production-grade solutions.

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