Data Scientist
Mahindra & Mahindra Limited Chakan, Maharashtra, India
Motor Vehicle Manufacturing · 10,001+ employees
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About the role
The candidate will design, build, and deploy machine learning models and data pipelines to drive enterprise-wide AI initiatives. They are also responsible for managing projects, prioritizing tasks, and communicating analytical findings to stakeholders.
What they look for
Requirements
Candidates must hold a degree in B.E, B.Tech, or M.Tech and possess 3-5 years of hands-on experience in data science or analytics. Strong proficiency in Python, SQL, and various machine learning libraries is required.
Full description
Job Purpose
A balanced mix of hands-on expertise as a data scientist and team management capabilities. The ideal candidate is someone who thrives at the intersection of technical depth and strategic impact. The candidate will own critical projects end-to-end to drive enterprise-wide AI initiatives.
Key Responsibilities
- Translate high-level business problems into analytical frameworks
- Design, build, and deploy machine learning models, statistical frameworks, and data pipelines.
- Perform deep data exploration and generate actionable insights.
- Work on a range of problems including predictive modeling, segmentation, recommendation systems, NLP, and computer vision depending on project needs.
- Conduct robust feature engineering, model evaluation, and tuning.
- Communicate findings clearly to technical and non-technical stakeholders.
- Manage timelines, prioritize tasks, and align with cross-functional stakeholder
Skills & Qualifications
- Degree in B.E / B.tech / M.Tech
- 3 - 5 years of hands-on experience in data science, machine learning, or analytics
- Strong programming skills in Python, SQL, and proficiency with libraries like scikit-learn, TensorFlow, PyTorch, or XGBoost.
- Strong understanding of supervised/unsupervised ML, A/B testing, and statistical modeling.
- Excellent communication, problem-solving, and stakeholder engagement skills.
- Experience working with both structured and unstructured data at scale.
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