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Lead Data scientist

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Lead Data scientist

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Job Title

Lead Data scientist

Job Description

Job title:

Lead Data scientist

Your role:

TheLead Data Scientistarchitects, builds, and runsproduction-gradeMachine Learning andGenerative AI systems—owning thefull lifecycle frommodel development toscalable cloud deployment and ongoingperformance monitoring. In addition, the role partners withcommercial stakeholders translate market/customer data intodecision-ready insights andAI-enabled analytics solutions that drivemeasurable outcomes

Operating with abuilder and translator mindset, the individual rapidly developsMVP analytics solutions, leveragesAI to accelerateinsight generation, and ensures strong product engineering fundamentals,data quality, andgovernance. The role plays a critical part in establishing asingle source of truth forperformance management across markets and channels while elevating analytics maturity from descriptive reporting topredictive andinsight-led decision making.

Key Responsibilities

1) ML & Deep Learning Model Development

Design,train, andoptimizeML models for prediction, classification, ranking,time-series forecasting,anomaly detection,NLP, andrecommendation use cases.
Build robustexperimentation workflows (train/validation strategy, ablations,error analysis) and improvemodel quality through iterative tuning.
Ensurereproducibility andmaintainability through clean code practices,versioning, andautomated testing.

2) GenAI Engineering (LLMs, RAG / MCP / fine-tuning, Agents)

Build enterprise-gradeLLM applications usingRAG (retrieval-augmented generation),MCP, andfine-tuning approaches:chunking strategies,embedding generation,hybrid retrieval,reranking,prompt templates, andcitation/attribution patterns.
DevelopLLM applications withtool use/function calling patterns andagentic workflows where appropriate.
Implementsystematic evaluation: curatedeval sets,prompt regression tests,hallucination checks,retrieval quality metrics, and automatedquality gates.

3) ML & LLM Operations: Productionization, Deployment & Monitoring

Deploy and operatereal-time andbatch inference solutions onAzure usingmanaged endpoints and/orcontainerized serving.
BuildCI/CD for ML systems:automated packaging,container builds,model validation tests,staged rollouts, androllback strategies.
Establishlifecycle management:model registry/versioning,lineage,promotion workflows, andrelease governance.
Implementobservability:latency,throughput,cost,drift signals,data quality checks,alerts, andperformance degradation monitoring.

4) Pipeline Orchestration & Automation (Train → Deploy)

Buildstandardized ML pipelines fortraining,evaluation, anddeployment usingorchestration tools (cloud-native pipelines and/or platform tools).
Automatedataset/version management,feature generation,scheduled retraining triggers, andapproval workflows.
Definerepeatable patterns forscalable experimentation andreliable production delivery.

5) Analytics Products, Dashboards & Data Governance

Own keyanalytics outputs asproducts (dashboards,reusable datasets,internal tools), continuously improving them based on usage patterns andperformance gaps.
Build and automatedashboards and analytical components using scalableSQL logic,Python transformations, andreusable modules.
Act as owner for criticalcommercial/syndicated datasets (e.g., GfK, Circana, Nielsen or equivalent):definitions,assumptions, andlimitations, ensuringtransparent logic andtrust in outputs.
Partner withdata engineering/IT to ensuredata quality,harmonization, andgovernance through strongvalidation andreconciliation practices.

6) Stakeholder Partnership & Decision Support (Lightweight, High Impact)

Serve astrusted analytics thought partner tosenior stakeholders (e.g., BU leadership, Sales, Marketing, Finance), shapingproblem statements and aligning onsuccess metrics.
Translate complex analytics into clearrecommendations with adecision-oriented storyline (“so-what / now-what”), tailored forleadership forums andreviews.
Supportperformance reviews,planning cycles, and high-priorityad-hoc requests withspeed,rigor, andconfidence; proactivelychallenge assumptions withfact-based insights.

7) Responsible AI, Security, and Risk Controls (GenAI-ready)

Implementguardrails:prompt injection defenses,sensitive data protections,output validation, andsecure tool execution patterns.
Applyresponsible AI practices: transparentevaluation criteria,auditability, andrisk controls aligned to enterprise needs.

8) Technical Leadership (Lead-level Expectations)

Setengineering standards for DS/ML codebases:design docs,code review practices,testing discipline, andproduction readiness checklists.
Mentor data scientists/ML engineers onmodeling,GenAI engineering, andMLOps best practices.
Leadarchitectural decisions across modeling approaches,retrieval stack,serving patterns, andevaluation strategy.

Core Skills & Competencies

Must-have (Technical)

StrongPython (production-quality coding) and solid CS fundamentals; strongSQL fordata access andvalidation.
Depth inML: TraditionalML exposure and at least onedeep learning framework (PyTorch/TensorFlow), with strong understanding ofmetrics andfailure modes.
GenAI implementation:RAG / MCP / fine-tuning,embeddings/vector search,prompt orchestration,evaluation harnesses, andLLM application patterns.
Production deployment experience onAWS orAzure (model/LLM app deployment,API serving,scaling,monitoring).
MLOps tooling:experiment tracking,model registry,CI/CD, andpipeline orchestration (e.g.,MLflow or equivalent patterns).

Good-to-have (Business + Influence)

Strongbusiness acumen and ability to connect disparatedata points intocompelling narratives that influencesenior stakeholders.
Builder/MVP mindset—rapid prototyping and iterating based onstakeholder feedback while maintainingdata quality andgovernance

Education Requirements

Bachelor’s degree inengineering,Computer Science,Statistics,Economics,Mathematics, or a relatedquantitative field.
Master’s degree preferred (e.g.,Data Analytics,Business Analytics,Applied Statistics,Economics,AI, orMBA with strong analytics focus).
Continuous learning mindset expected, with demonstratedupskilling inadvanced analytics,AI, ordata engineering concepts (formal or informal).

Note: This role valuesapplied problem-solving andbusiness impact over purely academic specialization.

You're the right fit if:

Proven track record of owningend-to-end analytics domains, not just contributing toisolated analyses or consumingpre-built reports.
7–12+ years in hands-onData Science / ML Engineering with multipleproduction deployments ownedend-to-end.
Demonstrated ability to take solutions fromexperimentation → production (reproducible pipelines, deployment tomanaged endpoints/container platforms,monitoring +iterative improvement).
StrongGenAI delivery record: shippedRAG/MCP/fine-tunedLLM applications with measurablequality controls,safety measures, andoperational readiness.
Experience operating incomplex, matrixed environments and partnering withsenior stakeholders to driveinsight-led decision making
Hands-on exposure toAI-enabled analytics, including the use ofGenAI tools (e.g.,ChatGPT,Claude, or similar) to accelerateinsight generation, analysis, orproductivity.
Strong experience partnering withsenior business stakeholders (BU leaders, Sales, Marketing, Finance),influencing decisions throughinsight-led storytelling.

#Personalhealth

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 company’s facilities.
Field roles are most effectively done outside of the company’s main facilities, generally at the customers’ or suppliers’ locations.
this role is anofficerole.

About Philips
We 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.
• Learn more aboutour business.
• Discoverour rich and exciting history.
• Learn more aboutour 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 carehere.

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by @maxrusakovic

Lead Data scientist

Job Title

Job Description

Your role:

Key Responsibilities

1) ML & Deep Learning Model Development

Design,train, andoptimizeML models for prediction, classification, ranking,time-series forecasting,anomaly detection,NLP, andrecommendation use cases.

Build robustexperimentation workflows (train/validation strategy, ablations,error analysis) and improvemodel quality through iterative tuning.

Ensurereproducibility andmaintainability through clean code practices,versioning, andautomated testing.

2) GenAI Engineering (LLMs, RAG / MCP / fine-tuning, Agents)

Build enterprise-gradeLLM applications usingRAG (retrieval-augmented generation),MCP, andfine-tuning approaches:chunking strategies,embedding generation,hybrid retrieval,reranking,prompt templates, andcitation/attribution patterns.

DevelopLLM applications withtool use/function calling patterns andagentic workflows where appropriate.

Implementsystematic evaluation: curatedeval sets,prompt regression tests,hallucination checks,retrieval quality metrics, and automatedquality gates.

3) ML & LLM Operations: Productionization, Deployment & Monitoring

Deploy and operatereal-time andbatch inference solutions onAzure usingmanaged endpoints and/orcontainerized serving.

BuildCI/CD for ML systems:automated packaging,container builds,model validation tests,staged rollouts, androllback strategies.

Establishlifecycle management:model registry/versioning,lineage,promotion workflows, andrelease governance.

Implementobservability:latency,throughput,cost,drift signals,data quality checks,alerts, andperformance degradation monitoring.

4) Pipeline Orchestration & Automation (Train → Deploy)

Buildstandardized ML pipelines fortraining,evaluation, anddeployment usingorchestration tools (cloud-native pipelines and/or platform tools).

Automatedataset/version management,feature generation,scheduled retraining triggers, andapproval workflows.

Definerepeatable patterns forscalable experimentation andreliable production delivery.

5) Analytics Products, Dashboards & Data Governance

Own keyanalytics outputs asproducts (dashboards,reusable datasets,internal tools), continuously improving them based on usage patterns andperformance gaps.

Build and automatedashboards and analytical components using scalableSQL logic,Python transformations, andreusable modules.

Act as owner for criticalcommercial/syndicated datasets (e.g., GfK, Circana, Nielsen or equivalent):definitions,assumptions, andlimitations, ensuringtransparent logic andtrust in outputs.

Partner withdata engineering/IT to ensuredata quality,harmonization, andgovernance through strongvalidation andreconciliation practices.

6) Stakeholder Partnership & Decision Support (Lightweight, High Impact)

Serve astrusted analytics thought partner tosenior stakeholders (e.g., BU leadership, Sales, Marketing, Finance), shapingproblem statements and aligning onsuccess metrics.

Translate complex analytics into clearrecommendations with adecision-oriented storyline (“so-what / now-what”), tailored forleadership forums andreviews.

Supportperformance reviews,planning cycles, and high-priorityad-hoc requests withspeed,rigor, andconfidence; proactivelychallenge assumptions withfact-based insights.

7) Responsible AI, Security, and Risk Controls (GenAI-ready)

Implementguardrails:prompt injection defenses,sensitive data protections,output validation, andsecure tool execution patterns.

Applyresponsible AI practices: transparentevaluation criteria,auditability, andrisk controls aligned to enterprise needs.

8) Technical Leadership (Lead-level Expectations)

Setengineering standards for DS/ML codebases:design docs,code review practices,testing discipline, andproduction readiness checklists.

Mentor data scientists/ML engineers onmodeling,GenAI engineering, andMLOps best practices.

Leadarchitectural decisions across modeling approaches,retrieval stack,serving patterns, andevaluation strategy.

Core Skills & Competencies

Must-have (Technical)

StrongPython (production-quality coding) and solid CS fundamentals; strongSQL fordata access andvalidation.

Depth inML: TraditionalML exposure and at least onedeep learning framework (PyTorch/TensorFlow), with strong understanding ofmetrics andfailure modes.

GenAI implementation:RAG / MCP / fine-tuning,embeddings/vector search,prompt orchestration,evaluation harnesses, andLLM application patterns.

Production deployment experience onAWS orAzure (model/LLM app deployment,API serving,scaling,monitoring).

MLOps tooling:experiment tracking,model registry,CI/CD, andpipeline orchestration (e.g.,MLflow or equivalent patterns).

Good-to-have (Business + Influence)

Strongbusiness acumen and ability to connect disparatedata points intocompelling narratives that influencesenior stakeholders.

Builder/MVP mindset—rapid prototyping and iterating based onstakeholder feedback while maintainingdata quality andgovernance

Education Requirements

Bachelor’s degree inengineering,Computer Science,Statistics,Economics,Mathematics, or a relatedquantitative field.

Master’s degree preferred (e.g.,Data Analytics,Business Analytics,Applied Statistics,Economics,AI, orMBA with strong analytics focus).

Continuous learning mindset expected, with demonstratedupskilling inadvanced analytics,AI, ordata engineering concepts (formal or informal).

Note: This role valuesapplied problem-solving andbusiness impact over purely academic specialization.

You're the right fit if:

Proven track record of owningend-to-end analytics domains, not just contributing toisolated analyses or consumingpre-built reports.

7–12+ years in hands-onData Science / ML Engineering with multipleproduction deployments ownedend-to-end.

Demonstrated ability to take solutions fromexperimentation → production (reproducible pipelines, deployment tomanaged endpoints/container platforms,monitoring +iterative improvement).

StrongGenAI delivery record: shippedRAG/MCP/fine-tunedLLM applications with measurablequality controls,safety measures, andoperational readiness.

Experience operating incomplex, matrixed environments and partnering withsenior stakeholders to driveinsight-led decision making

Hands-on exposure toAI-enabled analytics, including the use ofGenAI tools (e.g.,ChatGPT,Claude, or similar) to accelerateinsight generation, analysis, orproductivity.

Strong experience partnering withsenior business stakeholders (BU leaders, Sales, Marketing, Finance),influencing decisions throughinsight-led storytelling.