Lead Data Scientist / VP

Description

The role is titled Lead Data Scientist / VP and sits in the Specialized Analytics (Data Science/Computational Statistics) family within the Decision Management group.

You will do hands-on work across the full lifecycle of AI and analytics projects: designing analytical approaches, building and validating models, creating Generative AI and agentic workflows, and coordinating with the engineers who build data pipelines and with technology groups to put scalable production systems in place.

The role combines deep data science skill with practical engineering, delivery experience in a large enterprise, and a working knowledge of governance. You will also guide other data scientists and help set reusable methods, coding standards and delivery practices.

This opening fills an existing vacancy.

Responsibilities

• Frame workforce and organizational questions as testable hypotheses, select suitable methods, and turn the results into decisions.
• Build statistical analyses, predictive models, machine-learning solutions and analytical frameworks using regression, classification, clustering, survival analysis, forecasting, experimentation and causal inference.
• Handle feature engineering, variable selection, model tuning, validation, sensitivity tests and performance evaluation.
• Explain model behavior with SHAP, feature attribution and scenario analysis.
• Record assumptions, limitations, findings and business implications, and communicate them clearly.
• Design and build Generative AI applications, Retrieval-Augmented Generation solutions and agentic workflows.
• Implement tool calling, workflow orchestration, structured outputs, multi-turn conversations and state handling.
• Connect foundation models through approved enterprise gateways and cloud platforms.
• Create reusable prompts, agents, tools, evaluation components and analytical services.
• Build evaluation frameworks covering accuracy, groundedness, retrieval quality, task completion, consistency and hallucination risk.
• Tune solutions for reliability, speed, latency, cost and user experience.
• Design pipelines, analytical datasets, feature generation, APIs and production integrations alongside data engineers and architects.
• Write clean, reusable, well-documented Python and SQL for analytical and AI work.
• Work with structured and unstructured data from many enterprise sources.
• Add data-quality checks, logging, monitoring, error handling and traceability to analytical workflows.
• Help build feature libraries, embedding pipelines, vector search and model services.
• Take prototypes to scalable production using established MLOps and LLMOps practices.
• Make sure solutions meet Model Risk Management, privacy, cybersecurity, data governance and responsible AI requirements.
• Set access controls, data entitlements, PII protections, cohort-suppression rules and other analytical safeguards.
• Run performance, explainability, bias, fairness, stability and outcome tests on models.
• Support controls for prompt-injection detection, approved tool use, source grounding and output validation.
• Write documentation covering methods, data dependencies, testing, limitations, controls and implementation choices.
• Work with risk and control functions to resolve findings and support required reviews.
• Lead technical work on complex data science and AI initiatives while staying hands-on.
• Review peers' work and guide data scientists on related solutions.
• Help define standards for coding, analytical design, validation, testing, documentation and deployment.
• Work with product managers, data engineers, architects, technology teams and business stakeholders throughout delivery.
• Explain complex methods and results in plain language to business and senior audiences.
• Help with delivery planning, solution design, technical decisions and fixing implementation problems.

Requirements

• Six or more years of relevant experience in data science, machine learning, statistical modeling, advanced analytics, AI implementation or a related field.
• Proven record of delivering analytical, machine-learning or AI solutions in an enterprise setting.
• Experience taking work from research and prototyping through validation, production and monitoring.
• Ability to lead complex technical work on your own and collaborate across disciplines.
• Strong grounding in statistical modeling, probability, regression, classification, clustering, forecasting, experimentation and model validation.
• Advanced Python, including Pandas, NumPy, SciPy, Scikit-learn, Statsmodels, PyTorch or TensorFlow.
• Strong SQL, including data extraction, transformation, analytical cohort building and data-quality testing.
• Experience designing or building Generative AI, Retrieval-Augmented Generation, tool-calling or agentic applications.
• Familiarity with frameworks such as Google Agent Development Kit, LangGraph, LangChain, CrewAI or similar.
• Experience connecting foundation models through enterprise AI platforms such as Google Cloud and Vertex AI, Microsoft Azure or AWS.
• Understanding of APIs, data pipelines, vector embeddings, semantic search and production integration patterns.
• Working knowledge of MLOps and LLMOps: version control, testing, deployment, observability, monitoring and managing change.
• Grasp of responsible AI principles, explainability of models, bias and fairness checks, privacy and role-based access.
• Master's or Ph.D. in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, Operations Research or another quantitative field.
• A bachelor's degree with substantial relevant technical and implementation experience may also be considered.
• Master's degree in Analytics, Data Science or Computer Science.
• 6+ years of experience in Engineering or HR Technology.
• Experience with Python and JavaScript.
• Experience in AI Automation.
• Beneficial: experience with People Analytics, Workforce Planning, Human Capital Management, organizational effectiveness or financial-services analytics.
• Beneficial: experience with sensitive workforce data and governed analytical environments.
• Beneficial: experience operating in a regulated environment.
• Beneficial: familiarity with enterprise software development practices, source control, automated testing and CI/CD.
• Beneficial: strong written and verbal communication, including explaining technical methods to non-technical audiences.

Conditions

Full-time role.

Salary range for the primary location: $111,600.00 to $161,600.00.

Hiring decisions are made and checked by people. Automated processing and AI are used to match skills to roles, not to make autonomous decisions.

With your consent, your skills may be matched to other suitable roles at the company.

The company's equal employment opportunity policy applies.