Jio-bp

About Jio-bpJio-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 Chief Data Scientist will own Data Science strategy, delivery quality, and production outcomes for the AI CoE. This is a hands-on leadership role responsible for setting modeling and evaluation standards, driving measurable business impact, and ensuring solutions are production-grade, governed, and Responsible-AI aligned.You will lead a team of 8–10 Data Scientists and coordinate partner resources where applicable, working closely with AI Product, AI Architecture, Data Engineering, MLOps, Cloud, and Security/IRM teams to deliver scalable and reliable AI capabilities.Retail domain expertise is a must; Oil & Energy is a strong plus.Key Responsibilities<ul style="text-align: justify;"><li>DS Strategy, Standards & Quality Gates</li><li>Define the Data Science vision, modeling standards, and reusable playbooks across AI/advanced analytics and GenAI initiatives.</li><li>Establish evaluation frameworks, benchmarking approaches, and “quality gates” for production readiness (performance thresholds, stability checks, monitoring requirements, retraining triggers).</li><li>Build reusable assets: feature engineering patterns, experiment templates, model documentation packs, and governance artifacts.</li><li>End-to-End Delivery Leadership (Hands-on Oversight)</li><li>Lead problem framing with business stakeholders: objectives, constraints, assumptions, success metrics, and value hypotheses.</li><li>Oversee end-to-end DS delivery: EDA, feature strategy, modeling, tuning, validation, and readiness for deployment.</li><li>Ensure strong experimentation discipline: baselines, ablations, reproducibility, and robust testing for edge cases and data shifts.</li><li>Advanced Modeling Excellence</li><li>Drive excellence across predictive modeling: classification, regression, time-series/forecasting, anomaly detection, segmentation/clustering, and recommendation/personalization patterns.</li><li>Guide optimization/decision-support components where required, ensuring pragmatic assumptions and measurable outcomes.</li><li>Lead model selection and trade-offs across accuracy, interpretability, latency, scalability, and cost.</li><li>Production Readiness, Governance & Responsible AI</li><li>Establish DS governance: documentation standards, interpretability/explainability where required, bias/fairness checks, privacy and sensitive-data handling, and model risk documentation.</li><li>Ensure enterprise readiness with Security/IRM: auditability, traceability, approvals, environment discipline, and release evidence.</li><li>Define monitoring strategy and operating cadence: drift/quality/latency, business KPI impact, incident handling inputs, and retraining plans.</li><li>GenAI, Agentic AI Analytics & Evaluation (Where Applicable)</li><li>Define evaluation strategy for LLM, RAG, and Agentic AI system outputs: quality metrics, relevance, grounding, hallucination checks, task completion accuracy, safety signals, and feedback loops.</li><li>Drive structured testing and release gating evidence for GenAI and autonomous/agent-based AI systems, including human-in-the-loop evaluation where required.</li><li>Establish performance monitoring frameworks to assess agent decision flows, tool usage effectiveness, multi-step reasoning accuracy, and reliability in real-world scenarios.</li><li>Cross-Functional Delivery & Stakeholder Leadership</li><li>Partner with Data Engineering to ensure AI-ready datasets, labels, feature availability, data quality standards, and refresh SLAs.</li><li>Partner with MLOps/Platform to ensure deployability: model packaging/serving expectations, monitoring telemetry, and retraining automation triggers.</li><li>Provide clear leadership updates: progress, risks, mitigation, dependencies, and value realization tracking.</li><li>People Leadership (Team + Partners)</li><li>Lead and mentor a team of 8–10 Data Scientists; guide performance, capability building, and delivery accountability.</li><li>Coordinate partner resources where applicable to ensure quality, velocity, and adherence to standards.</li><li>Set strong engineering and DS practices: code reviews, documentation, experiment tracking, and knowledge sharing.</li></ul>Must-Have Skills & Experience<ul style="text-align: justify;"><li>B.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.</li><li>12–15+ years in Data Science/Applied ML, including 5+ years leading teams/pods and owning production outcomes.</li><li>Strong hands-on depth in statistics and ML, with rigorous evaluation practices (AUC/F1, precision/recall, calibration, lift, forecasting metrics, stability testing; causal thinking where relevant).</li><li>Proven expertise in feature engineering, experiment design, model selection, and translating business problems into deployable ML solutions.</li><li>Production mindset: monitoring/drift strategy, retraining approach, model risk management, and strong documentation discipline.</li><li>Strong Python (and working SQL); experience with modern data/AI platforms (Databricks preferred) and large-scale datasets.</li><li>Strong stakeholder management and communication across business, engineering, and governance teams.</li><li>Retail domain expertise (Must); Oil & Energy (big plus).</li><li>Authored whitepapers, publications, or patents in applied ML/AI (preferred).</li></ul>Why Join Jio-bp AI CoE<ul><li style="text-align: justify;">Opportunity to build and shape an enterprise-grade AI/GenAI platform on Azure Databricks from the ground up.</li><li style="text-align: justify;">Work at the intersection of energy, mobility, and digital innovation with high-visibility, high-impact programs.</li><li style="text-align: justify;">Collaborate with cross-functional teams and contribute to RIL’s broader AI transformation agenda.</li><li style="text-align: justify;">Be a core builder of the AI CoE foundation, driving measurable business value across Jio-bp operations.</li></ul>

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