{"id":3514832,"url":"https://alion.io/job/philips-lead-data-scientist","title":"Lead Data Scientist","company":{"id":13838,"name":"Philips","domain":"philips.com","url":"https://alion.io/company/philips-2","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":82,"open_postings":68,"ghost_share":0,"stale_share":0.897,"repost_share":0.015,"time_to_fill_p50_days":11,"computed_at":"2026-10-31T05:58:55Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":38000,"max_usd":81000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":20},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"Hallucination","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"NLP","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"Time Series","optional":false},{"name":"ChatGPT","optional":true},{"name":"Claude","optional":true}],"status":"live","first_seen_at":"2026-10-29T00:00:00Z","employer_posted_date":"2026-10-29","last_verified_at":"2026-10-31T06:02:00Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Job Title\nLead Data ScientistJob Description\nJob title:\nLead Data Scientist\nYour role:\nThe Lead Data Scientist architects, builds, and runs production-grade Machine Learning and Generative AI systems-owning 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\nOperating 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.\nKey Responsibilities\n1) ML & Deep Learning Model Development\nDesign, train, and optimize ML models for prediction, classification, ranking, time-series forecasting, anomaly detection, NLP, and recommendation use cases.\n\nBuild robust experimentation workflows (train/validation strategy, ablations, error analysis) and improve model quality through iterative tuning.\n\nEnsure reproducibility and maintainability through clean code practices, versioning, and automated testing.\n\n2) GenAI Engineering (LLMs, RAG / MCP / fine-tuning, Agents)\nBuild 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.\n\nDevelop LLM applications with tool use /function calling patterns and agentic workflows where appropriate.\n\nImplement systematic evaluation: curated eval sets, prompt regression tests, hallucination checks, retrieval quality metrics, and automated quality gates.\n\n3) ML & LLM Operations: Productionization, Deployment & Monitoring\nDeploy and operate real-time and batch inference solutions on Azure using managed endpoints and/or containerized serving.\n\nBuild CI/CD for ML systems: automated packaging, container builds, model validation tests, staged rollouts, and rollback strategies.\n\nEstablish lifecycle management: model registry /versioning, lineage, promotion workflows, and release governance.\n\nImplement observability: latency, throughput, cost, drift signals, data quality checks, alerts, and performance degradation monitoring.\n\n4) Pipeline Orchestration & Automation (Train → Deploy)\nBuild standardized ML pipelines for training, evaluation, and deployment using orchestration tools (cloud-native pipelines and/or platform tools).\n\nAutomate dataset/version management, feature generation, scheduled retraining triggers, and approval workflows.\n\nDefine repeatable patterns for scalable experimentation and reliable production delivery.\n\n5) Analytics Products, Dashboards & Data Governance\nOwn key analytics outputs as products (dashboards, reusable datasets, internal tools), continuously improving them based on usage patterns and performance gaps.\n\nBuild and automate dashboards and analytical components using scalable SQL logic, Python transformations, and reusable modules.\n\nAct 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.\n\nPartner with data engineering/IT to ensure data quality, harmonization, and governance through strong validation and reconciliation practices.\n\n6) Stakeholder Partnership & Decision Support (Lightweight, High Impact)\nServe as trusted analytics thought partner to senior stakeholders (e.g., BU leadership, Sales, Marketing, Finance), shaping problem statements and aligning on success metrics.\n\nTranslate complex analytics into clear recommendations with a decision-oriented storyline (“so-what / now-what ”), tailored for leadership forums and reviews.\n\nSupport performance reviews, planning cycles, and high-priority ad-hoc requests with speed, rigor, and confidence; proactively challenge assumptions with fact-based insights.\n\n7) Responsible AI, Security, and Risk Controls (GenAI-ready)\nImplement guardrails: prompt injection defenses, sensitive data protections, output validation, and secure tool execution patterns.\n\nApply responsible AI practices: transparent evaluation criteria, auditability, and risk controls aligned to enterprise needs.\n\n8) Technical Leadership (Lead-level Expectations)\nSet engineering standards for DS/ML codebases: design docs, code review practices, testing discipline, and production readiness checklists.\n\nMentor data scientists/ML engineers on modeling, GenAI engineering, and MLOps best practices.\n\nLead architectural decisions across modeling approaches, retrieval stack, serving patterns, and evaluation strategy.\n\nCore Skills & Competencies\nMust-have (Technical)\nStrong Python (production-quality coding) and solid CS fundamentals; strong SQL for data access and validation.\n\nDepth in ML: Traditional ML exposure and at least one deep learning framework (PyTorch/TensorFlow), with strong understanding of metrics and failure modes.\n\nGenAI implementation: RAG / MCP / fine-tuning, embeddings/vector search, prompt orchestration, evaluation harnesses, and LLM application patterns.\n\nProduction deployment experience on AWS or Azure (model/LLM app deployment, API serving, scaling, monitoring).\n\nMLOps tooling: experiment tracking, model registry, CI/CD, and pipeline orchestration (e.g., MLflow or equivalent patterns).\n\nGood-to-have (Business + Influence)\nStrong business acumen and ability to connect disparate data points into compelling narratives that influence senior stakeholders.\n\nBuilder/MVP mindset -rapid prototyping and iterating based on stakeholder feedback while maintaining data quality and governance\n\nEducation Requirements\nBachelor’s degree in engineering, Computer Science, Statistics, Economics, Mathematics, or a related quantitative field.\n\nMaster’s degree preferred (e.g., Data Analytics, Business Analytics, Applied Statistics, Economics, AI, or MBA with strong analytics focus).\n\nContinuous learning mindset expected, with demonstrated upskilling in advanced analytics, AI, or data engineering concepts (formal or informal).\n\nNote: This role values applied problem-solving and business impact over purely academic specialization.\nYou're the right fit if:\nProven track record of owning end-to-end analytics domains, not just contributing to isolated analyses or consuming pre-built reports.\n\n7-12+ years in hands-on Data Science / ML Engineering with multiple production deployments owned end-to-end.\n\nDemonstrated ability to take solutions from experimentation → production (reproducible pipelines, deployment to managed endpoints /container platforms, monitoring + iterative improvement).\n\nStrong GenAI delivery record: shipped RAG/MCP/fine-tuned LLM applications with measurable quality controls, safety measures, and operational readiness.\n\nExperience operating in complex, matrixed environments and partnering with senior stakeholders to drive insight-led decision making\n\nHands-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.\n\nStrong experience partnering with senior business stakeholders (BU leaders, Sales, Marketing, Finance), influencing decisions through insight-led storytelling.\n\nHow we work together\nWe believe that we are better together than apart. For our office-based teams, this means working in-person at least 3 days per week.\nOnsite roles require full-time presence in the company’s facilities.\nField roles are most effectively done outside of the company’s main facilities, generally at the customers’ or suppliers’ locations.\nthis role is an office role.\nAbout Philips\nWe are a health technology company. We built our entire company around the belief that every human matters, and we won't stop until everybody everywhere has access to the quality healthcare that we all deserve. Do the work of your life to help the lives of others.\nLearn more about our business.\nDiscover our rich and exciting history.\nLearn more about our purpose. If you’re interested in this role and have many, but not all, of the experiences needed, we encourage you to apply. You may still be the right candidate for this or other opportunities at Philips. Learn more about our culture of impact with care here.","description_format":"text","description_chars":8632,"description_truncated":false,"requirements":{"experience_years_min":7,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Continuous learning"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Health & Medical Electronics","Respiratory & Sleep Devices","Patient Monitoring","EHR & Clinical Software"],"lifecycle":[{"event":"open","at":"2026-10-30T09:49:50Z"}],"visa":[],"liveness":{"score":63,"band":"ok","label":"Likely open","p_open":1,"p_active":0.632,"p_room":1,"age_days":2,"expected_fill_days":11,"reasons":["conf:4","stale_co","velocity","win:early","comp:brand"],"computed_at":"2026-10-31T05:58:55Z"},"pay":null,"html_url":"https://alion.io/job/philips-lead-data-scientist","json_url":"https://alion.io/job/philips-lead-data-scientist.json","meta":{"generated_at":"2026-10-31T19:00:04Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":29814,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-01T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":13838},"rest":"https://alion.io/mcp/rest/get_company?id=13838"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fphilips-lead-data-scientist"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fphilips-lead-data-scientist"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fphilips-lead-data-scientist"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/philips-lead-data-scientist\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fphilips-lead-data-scientist"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":3514832}},{"tool":"search_jobs","arguments":{"company":"philips.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":3514832},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. Docs: https://alion.io/developers"}}