Lead Data scientist

Job Title

Job Description

Job title:

Lead Data scientist

Your role:

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.

Youre 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.

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How we work together
We believe that we are better together than apart. For our office-based teams this means working in-person at least 3 days per week.
Onsite roles require full-time presence in the companys facilities.
Field roles are most effectively done outside of the companys main facilities generally at the customers or suppliers locations.
this role is an office role.

About Philips
We are a health technology company. We built our entire company around the belief that every human matters and we wont 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.
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Discover our rich and exciting history.
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If youre 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.