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ABOUT THE ROLE
T7E Aftermarket Connect is building a Trade Growth Engine — an intelligent, data-driven platform that powers loyalty, rewards, and channel activation for leading brands across Auto Aftermarket, Building & Construction, and Agriculture. As our Machine Learning Engineer, you will be the architect of the intelligence layer: building models that predict behaviour, personalise engagement, and drive measurable outcomes for our clients' trade ecosystems. This is a founding ML role with direct visibility to the COO and CEO.
WHAT YOU WILL BUILD
• Recommendation and propensity models that drive personalised loyalty offers and mechanic/retailer engagement nudges
• Demand forecasting and redemption prediction pipelines for our rewards fulfilment engine
• Churn prediction and re-engagement scoring across 200K+ trade partner profiles
• Segmentation and clustering models to enable targeted campaign execution for brand clients
• Dashboards and APIs that surface ML outputs to product, ops, and client-facing teams
• Data pipelines and feature stores to ensure clean, consistent ML-ready data across platforms
WHAT WE ARE LOOKING FOR
• 3–6 years of hands-on ML engineering experience — not just research, but deployed, production models
• Strong background in e-commerce, loyalty, fintech, or consumer platforms where ML drove business outcomes
• Proficiency in Python (scikit-learn, XGBoost, TensorFlow or PyTorch), SQL, and REST APIs
• Experience building recommendation systems, propensity models, or NLP-driven personalisation
• Comfort working with messy, real-world transactional data — field ops, POS, scan logs, redemption records
• Ability to communicate model outputs to non-technical stakeholders — client teams, ops, and leadership
• Based in or willing to relocate to the Central Line / Navi Mumbai / Thane corridor (office: Mulund West)
GOOD TO HAVE
• Experience with MLOps tooling — MLflow, Airflow, Docker, or cloud ML platforms (AWS SageMaker / GCP Vertex)
• Exposure to GenAI or LLM-based applications (RAG, fine-tuning, prompt engineering)
• Prior work in trade marketing, channel loyalty, or distribution ecosystem analytics
• Knowledge of Marathi or Hindi — helpful for understanding field data context
TECH ENVIRONMENT
Python SQL / MySQL
REST APIs
Node.js stack
WHY T7E
• 12-year-old profitable company scaling from ₹20 Cr to ₹100 Cr — ML is central to that journey, not a side experiment
• Direct access to real trade data: scan logs, loyalty transactions, FSO field activity across 5+ marquee clients
• Lean, senior team — you will own the ML charter end to end, not be a cog in a large data science org
• SME IPO on the horizon — early equity conversation possible for the right profile
• Clients include ENI, Castrol, Gulf, Akzo Nobel, Michelin, Pidilite, SKF — real industry depth