Lead Data Scientist / VP

Description

You will work hands-on across the full AI and analytics lifecycle, designing analytical approaches, building and validating models, creating Generative AI and agentic workflows, and working with data engineers and technology teams to put scalable production systems in place.

The person in this role pairs strong data-science skill with engineering practice, experience delivering in an enterprise, and a working understanding of governance. You will also guide other data scientists and help set reusable methods, coding standards and implementation practices.

Responsibilities

• Turn workforce and organizational questions into testable hypotheses, suitable methods and insights the business can act on.
• 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 testing and performance evaluation.
• Apply explainability methods such as SHAP, feature attribution and scenario analysis.
• Document 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 interactions and state management.
• Connect foundation models through approved enterprise gateways and cloud platforms.
• Create reusable prompts, agents, tools, evaluation components and analytical services.
• Build evaluation frameworks that measure accuracy, groundedness, retrieval quality, task completion, consistency and hallucination risk.
• Tune solutions for reliability, performance, latency, cost and user experience.
• Work with data engineers and architects on pipelines, analytical datasets, feature generation, APIs and production integrations.
• Write clean, reusable, well-documented Python and SQL for analytical and AI work.
• Work with structured and unstructured data from several enterprise sources.
• Add data-quality checks, logging, monitoring, error handling and traceability to analytical workflows.
• Support feature libraries, embedding pipelines, vector search and model services.
• Take prototypes into scalable production using established MLOps and LLMOps practices.
• Make sure solutions meet Model Risk Management, privacy, cybersecurity, data-governance and responsible AI requirements.
• Set up access controls, data entitlements, PII protections, cohort-suppression rules and other analytical safeguards.
• Test models for performance, explainability, bias, fairness, stability and outcomes.
• Support controls for prompt-injection detection, approved tool use, source grounding and model-output validation.
• Write documentation covering methods, data dependencies, testing, limitations, controls and implementation decisions.
• Work with risk and control functions to resolve findings and support required reviews.
• Act as technical lead on complex data-science and AI initiatives while remaining hands-on.
• Review the work of data scientists on related solutions and give them technical guidance.
• Help set standards for coding, analytical design, model validation, testing, documentation and deployment.
• Work with product managers, data engineers, architects, technology teams and business stakeholders throughout implementation.
• Explain complex technical concepts and analytical results clearly to business and senior audiences.
• Contribute to delivery planning, solution design, technical decisions and resolving implementation issues.

Requirements

• You have 6+ years of relevant experience in data science, machine learning, statistical modeling, advanced analytics, AI implementation or a similar field.
• You have shown you can implement analytical, machine-learning or AI solutions in an enterprise setting.
• You have taken solutions from research and prototyping through validation, production deployment and monitoring.
• You can lead complex technical work independently and work effectively across multidisciplinary teams.
• Strong knowledge of statistical modeling, probability, regression, classification, clustering, forecasting, experimentation and model validation.
• Advanced Python skills, with libraries such as Pandas, NumPy, SciPy, Scikit-learn, Statsmodels, PyTorch or TensorFlow.
• Strong SQL, including data extraction, transformation, analytical cohort development 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 comparable technologies.
• Experience integrating 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.
• Familiarity with MLOps or LLMOps, including versioning, testing, deployment, observability, monitoring and change management.
• Knowledge of responsible AI, model explainability, bias and fairness testing, data privacy and role-based access controls.
• Experience with Python and JavaScript.
• Experience with AI automation.
• 6+ years of experience in engineering or HR technology.
• A master's or Ph.D. in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, Operations Research or another quantitative discipline.
• A master's degree in Analytics, Data Science or Computer Science.
• A bachelor's degree with substantial relevant technical and implementation experience may also be considered.
• Beneficial: experience in People Analytics, Workforce Planning, Human Capital Management, organizational effectiveness or financial-services analytics.
• Beneficial: experience operating within a regulated environment.
• Beneficial: experience working with sensitive workforce data and governed analytical environments.
• 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 in the Decision Management group, within the Specialized Analytics (Data Science/Computational Statistics) job family.

Salary range: $111,600.00 - $161,600.00.

This is an existing vacancy.

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The company is an equal-opportunity employer; see its EEO policy statement and Know Your Rights poster.