Lead Data Scientist
- Philips
- 6 - 11 years
- Bengaluru
- 16 days ago
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Job Description
The Lead Data Scientist architects, builds, and runs production-grade Machine Learning and Generative AI systemsowning the full lifecycle from model development to scalable cloud deployment and ongoing performance monitoring. In addition, the role partners with commercial stakeholders translate market/customer data into decision-ready insights and AI-enabled analytics solutions that drive measurable outcomes
Operating with a builder and translator mindset, the individual rapidly develops MVP analytics solutions, leverages AI to accelerate insight generation, and ensures strong product engineering fundamentals, data quality, and governance. The role plays a critical part in establishing a single source of truth for performance management across markets and channels while elevating analytics maturity from descriptive reporting to predictive and insight-led decision making.
Key Responsibilities:
1) ML & Deep Learning Model Development
- Design, train, and optimize ML models for prediction, classification, ranking, time-series forecasting, anomaly detection, NLP, and recommendation use cases.
- Build robust experimentation workflows (train/validation strategy, ablations, error analysis) and improve model quality through iterative tuning.
- Ensure reproducibility and maintainability through clean code practices, versioning, and automated testing.
2) GenAI Engineering (LLMs, RAG / MCP / fine-tuning, Agents)
- Build enterprise-grade LLM applications using RAG (retrieval-augmented generation), MCP, and fine-tuning approaches: chunking strategies, embedding generation, hybrid retrieval, reranking, prompt templates, and citation/attribution patterns.
- Develop LLM applications with tool use/function calling patterns and agentic workflows where appropriate.
- Implement systematic evaluation: curated eval sets, prompt regression tests, hallucination checks, retrieval quality metrics, and automated quality gates.
3) ML & LLM Operations: Productionization, Deployment & Monitoring
- Deploy and operate real-time and batch inference solutions on Azure using managed endpoints and/or containerized serving.
- Build CI/CD for ML systems: automated packaging, container builds, model validation tests, staged rollouts, and rollback strategies.
- Establish lifecycle management: model registry/versioning, lineage, promotion workflows, and release governance.
- Implement observability: latency, throughput, cost, drift signals, data quality checks, alerts, and performance degradation monitoring.
4) Pipeline Orchestration & Automation (Train Deploy)
- Build standardized ML pipelines for training, evaluation, and deployment using orchestration tools (cloud-native pipelines and/or platform tools).
- Automate dataset/version management, feature generation, scheduled retraining triggers, and approval workflows.
- Define repeatable patterns for scalable experimentation and reliable production delivery.
5) Analytics Products, Dashboards & Data Governance
- Own key analytics outputs as products (dashboards, reusable datasets, internal tools), continuously improving them based on usage patterns and performance gaps.
- Build and automate dashboards and analytical components using scalable SQL logic, Python transformations, and reusable modules.
- Act as owner for critical commercial/syndicated datasets (e.g., GfK, Circana, Nielsen or equivalent): definitions, assumptions, and limitations, ensuring transparent logic and trust in outputs.
- Partner with data engineering/IT to ensure data quality, harmonization, and governance through strong validation and reconciliation practices.
6) Stakeholder Partnership & Decision Support (Lightweight, High Impact)
- Serve as trusted analytics thought partner to senior stakeholders (e.g., BU leadership, Sales, Marketing, Finance), shaping problem statements and aligning on success metrics.
- Translate complex analytics into clear recommendations with a decision-oriented storyline (so-what / now-what), tailored for leadership forums and reviews.
- Support performance reviews, planning cycles, and high-priority ad-hoc requests with speed, rigor, and confidence; proactively challenge assumptions with fact-based insights.
7) Responsible AI, Security, and Risk Controls (GenAI-ready)
- Implement guardrails: prompt injection defenses, sensitive data protections, output validation, and secure tool execution patterns.
- Apply responsible AI practices: transparent evaluation criteria, auditability, and risk controls aligned to enterprise needs.
8) Technical Leadership (Lead-level Expectations)
- Set engineering standards for DS/ML codebases: design docs, code review practices, testing discipline, and production readiness checklists.
- Mentor data scientists/ML engineers on modeling, GenAI engineering, and MLOps best practices.
- Lead architectural decisions across modeling approaches, retrieval stack, serving patterns, and evaluation strategy.
Core Skills & Competencies
Must-have (Technical)
- Strong Python (production-quality coding) and solid CS fundamentals; strong SQL for data access and validation.
- Depth in ML: Traditional ML exposure and at least one deep learning framework (PyTorch/TensorFlow), with strong understanding of metrics and failure modes.
- GenAI implementation: RAG / MCP / fine-tuning, embeddings/vector search, prompt orchestration, evaluation harnesses, and LLM application patterns.
- Production deployment experience on AWS or Azure (model/LLM app deployment, API serving, scaling, monitoring).
- MLOps tooling: experiment tracking, model registry, CI/CD, and pipeline orchestration (e.g., MLflow or equivalent patterns).
Good-to-have (Business + Influence)
- Strong business acumen and ability to connect disparate data points into compelling narratives that influence senior stakeholders.
- Builder/MVP mindsetrapid prototyping and iterating based on stakeholder feedback while maintaining data quality and governance
Education Requirements
- Bachelors degree in engineering, Computer Science, Statistics, Economics, Mathematics, or a related quantitative field.
- Masters degree preferred (e.g., Data Analytics, Business Analytics, Applied Statistics, Economics, AI, or MBA with strong analytics focus).
- Continuous learning mindset expected, with demonstrated upskilling in advanced analytics, AI, or data engineering concepts (formal or informal).
Note: This role values applied problem-solving and business impact over purely academic specialization.
You're the right fit if:
- Proven track record of owning end-to-end analytics domains, not just contributing to isolated analyses or consuming pre-built reports.
- 712+ years in hands-on Data Science / ML Engineering with multiple production deployments owned end-to-end.
- Demonstrated ability to take solutions from experimentation production (reproducible pipelines, deployment to managed endpoints/container platforms, monitoring + iterative improvement).
- Strong GenAI delivery record: shipped RAG/MCP/fine-tuned LLM applications with measurable quality controls, safety measures, and operational readiness.
- Experience operating in complex, matrixed environments and partnering with senior stakeholders to drive insight-led decision making
- Hands-on exposure to AI-enabled analytics, including the use of GenAI tools (e.g., ChatGPT, Claude, or similar) to accelerate insight generation, analysis, or productivity.
- Strong experience partnering with senior business stakeholders (BU leaders, Sales, Marketing, Finance), influencing decisions through insight-led storytelling.
Job Classification
Industry: Consumer Electronics & AppliancesFunctional Area / Department: Data Science & AnalyticsRole Category: Data Science & Machine LearningRole: Data ScientistEmployement Type: Full timeContact Details:
Company: PhilipsLocation(s): Bengaluru
Keyskills: GenAI Engineering Data Science Artificial Intelligence Statistical Modeling RAG retrieval-augmented generation LLM Machine Learning Statistics Deep Learning Python
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