Jio-bp

Jio-bp (Reliance BP Mobility Limited) is a joint venture between Reliance Industries Limited (RIL) and bp, committed to transforming the mobility and energy landscape in India. Jio-bp delivers world-class fuels, mobility solutions, and customer experiences while advancing sustainability and digital innovation. As part of its digital transformation journey, Jio-bp is establishing an AI Center of Excellence (AI CoE) to drive data-led decision-making, innovation, and operational excellence across business functions.Role OverviewThe Lead Data Scientist will own end-to-end delivery of AI/ML initiatives—from problem framing to production outcomes—while leading a small pod and coordinating partner resources where applicable. This is a hands-on role requiring strong modeling depth, structured experimentation, and strong collaboration with AI Product, AI Architecture, Data Engineering, MLOps, Cloud, and Security/IRM teams to ensure solutions are production-ready, governed, and measurable.Retail domain expertise is a must; Oil & Energy is a strong plus.Key ResponsibilitiesProblem Framing & Business Analysis Partner with business SPOCs to map workflows, define problem statements, constraints, assumptions, and measurable success metrics. Translate requirements into an analytical approach: data needs, target definition/labeling, evaluation plan, and expected impact.Hands-on Modeling, Agentic AI & Experimentation Build and validate ML models (classification, regression, time-series/forecasting, anomaly detection, segmentation/clustering; recommendation concepts where relevant), and contribute to LLM-powered and agentic AI workflows where decision-making involves multi-step reasoning or tool usage. Drive feature engineering strategy and experimentation discipline: baselines, ablations, hyperparameter tuning, robustness checks, and reproducibility, including evaluation of agent reasoning paths, tool selection effectiveness, and task success rates in agent-based systems. Deep-dive on data quality, leakage, bias, and stability to ensure models generalize and remain operationally feasible.Production Readiness & MLOps Partnership Define deployment requirements with MLOps: inference approach, performance SLAs, monitoring metrics (drift/quality/latency), and retraining triggers. Support UAT readiness: model evidence, documentation, and explainability where needed; ensure smooth handover to production operations. Collaborate with Data Engineering to ensure AI-ready datasets, pipelines, refresh SLAs, and feature availability.Governance & Responsible AI Ensure governance compliance: model documentation, auditability, privacy-by-design, and Responsible AI controls aligned with Security/IRM. Implement interpretability and bias/fairness checks where applicable; maintain model risk and testing artifacts.Delivery Leadership & Stakeholder Management Lead a DS pod: tasking, mentoring, code reviews, and delivery quality; coordinate partner resources where applicable. Optionally manage a small team (as the CoE scales): onboarding, capability building, and performance feedback. Communicate progress, risks, and mitigation clearly; manage dependencies with Product, Engineering, and Platform teams. Track adoption and impact metrics post-release; iterate based on business feedback and monitoring insights.Must-Have Skills & ExperienceB.Tech./B.E. or Master’s in Statistics, AI, Economics, Mathematics (or related field) with strong R&D/research experience; PhD in Statistics/AI (or equivalent) is highly preferred. 8–12 years in Data Science/Applied ML with proven ownership of production outcomes; experience leading pods/mentoring DS. Strong hands-on depth in statistics and ML, including evaluation rigor (AUC/F1, precision/recall, calibration, lift, forecasting metrics, stability testing). Strong expertise in feature engineering, experiment design, model selection, and translating business problems into deployable solutions. Strong Python (and working SQL); experience with Databricks/Spark and large datasets is a strong plus. Strong understanding of production DS: monitoring/drift strategy, retraining approach, and documentation discipline. Strong collaboration and stakeholder communication across business, engineering, and governance teams. Retail domain expertise (Must); Oil & Energy (big plus).Why Join Jio-bp AI CoEOpportunity to build and shape an enterprise-grade AI/GenAI platform on Azure Databricks from the ground up. Work at the intersection of energy, mobility, and digital innovation with high-visibility, high-impact programs. Collaborate with cross-functional teams and contribute to RIL’s broader AI transformation agenda. Be a core builder of the AI CoE foundation, driving measurable business value across Jio-bp operations.

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