Role Overview

We’re building a multi-disciplinary engineering team that blends platform, application, and development expertise to deliver AI-driven analytics solutions. This role spearheads end-to-end execution—from requirement analysis to regional deployment. The position demands strong expertise in predictive modelling, time-series analytics, and scalable AI infrastructure, with a focus on driving operational efficiency and quality across global manufacturing operations

Key Responsibilities

• Strategic Leadership: Translate ambiguous business problems into clear, actionable data science strategies.
• Advanced Modeling: Design, build, and deploy machine learning algorithms, deep learning models, and forecasting systems.
• Cross-functional Collaboration: Work alongside data engineering, software, and MLOps teams to integrate models into production environments.
• Data Governance & Strategy: Identify new data sources, define company data assets, and ensure robust model monitoring and lifecycle management.
• Training & Development: Develop and grow in-house data analysts.

Enablement Scope

• Licenses & Tools: Manage provisioning and lifecycle of AI platforms, APIs, and development environments (e.g., AWS, Azure ML, Hugging Face).
• Infrastructure: Establish resilient computing clusters and data pipelines for high-performance AI execution and data handling.

Assessment: Implement structured evaluation frameworks to measure model performance, delivery success, and AI adoption

Strategic Impact

• Accelerate AI adoption across the Asia Pacific region through scalable infrastructure and structured delivery.

• Enable cross-functional innovation by aligning AI capabilities with manufacturing and business objectives.

• Drive measurable outcomes in quality, efficiency, and operational optimization through defined KPIs and success metrics.

Qualifications

• Education: Bachelor’s degree in computer science or Master’s in statistics or mathematics.
• Industry Experience 7+ years of experience leading data science projects in an automotive Industry.
• Programming Languages: High proficiency in Python, R, or Java. Advanced SQL for complex querying.
• Tools & Frameworks:
• ML Frameworks (TensorFlow, PyTorch, Scikit-learn, XGBoost)
• Big Data (Spark, Hive, Hadoop)
• BI & Visualization (Tableau, Power BI)
• Cloud Platforms (Databricks, Snowflakes, AWS, Azure, or GCP)
• Core Competencies: Strong grasp of exploratory data analysis (EDA), feature engineering, A/B testing, and dynamic model tuning.

Prior Automotive experience especially in EE Architecture, Hardware engineering and EE validation will be an added advantage

Preferred Traits

• Driving new data insights to uncover business opportunities & revenue streams.
• Have a firm understanding of statistics and data visualization for communication.
• Have an understanding of business growth areas & how to utilize important data.
• Be responsible for identifying new analytic trends/ data needs and repeatable analytic solutions.
• Able to work with key stakeholders to generate hypotheses and create analytic models that answer impactful business questions
• Identifies, analyzes and interprets trends or patterns in complex data sets using various supervised and unsupervised ML algorithms such as regression, classification or clustering ML approaches where applicable.
• Be experienced in Data Wrangling and Dynamic Model Tuning.

Please share your profiles to careers.swxindia@stellantis.com with below details:

Total yrs. of exp:

CCTC:

ECTC:

NP:

Disclaimer - At Stellantis, we assess candidates based on qualifications, merit and business needs. We welcome applications from people of all gender identities, age, ethnicity, nationality,

religion, sexual orientation and disability. Diverse teams will allow us to

better meet the evolving needs of our customers and care for our future.

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