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Data Scientist
Reads like a ML Engineer role: putting models into production.
Mumbai - Worli, Mumbai - Worli, IN
Actively hiring, first seen by us today- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
- Not stated
What you’ll work on
Full postingWe 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.
This is not a reporting-only analytics role—you will lead initiative delivery, mentor engineers, and build scalable ML systems end-to-end.
Design ML/DL models for structured and time-series data
Own sensor data pipelines: cleaning, windowing, filtering and feature extraction
Build and maintain end-to-end ML pipelines in Python
Deploy models with Docker, FastAPI and MLflow
Monitor, retrain and govern model performance over time
From the employer’s posting
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.
Design and implement ML/DL architectures for structured and time-series data
Own sensor data pipelines: synchronization, resampling, windowing, denoising, drift handling, filtering, and feature extraction.
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.
What you’ll bring
All qualificationsCore experience
- 3-5 Years of relevant experience
- Strong experience with time-series sensor data (automotive/IoT/industrial/telemetry), including sequence modeling and feature extraction.
Qualification wording
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.
Posting record
Report an errorActively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 10, 2026today
- Recorded sightings
- 1
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Mahindra & Mahindra is hiring
6 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- AWS
- Azure
- Docker
- GCP
- MLflow
- FastAPI
- NumPy
- pandas
- PyTorch
- scikit-learn
- TensorFlow
- XGBoost
Where the posting names them
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
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.
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
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.
Your next step
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Demand for ML Engineer roles
New ML Engineer postings at the employers we follow are up 13% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
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.
Our commitment to Diversity, Equity, and Inclusion
Source details
Source notes
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
Mumbai - Worli, Mumbai - Worli, IN
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.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.