Senior Analyst - Data Science

Job Summary

The Senior Analyst - Data Science provides advanced, cross-functional advisory leadership for the Human Resources Group (HRG). This role is used for complex, ambiguous, or enterprise-impacting work that requires deep functional expertise, strong stakeholder influence, standards-setting, and the ability to connect business outcomes, data, technology, process, product, and AI-readiness considerations across multiple teams or portfolios.

The Senior Analyst - Data Science supports HRG by leading statistical analysis efforts, deploying production ready machine learning models, experimentation, forecasting, Large Language models, and advanced analytics techniques to workforce and HR business problems. This role leads the partnership with HR leaders, data engineers, data analysts, business insights analysts, product teams, and governance partners to develop explainable, ethical, and actionable analytical solutions.

The role is responsible for framing analytical problems, preparing data, building and evaluating models, ML Ops, communicating findings, and supporting responsible deployment of predictive or AI-enabled insights. This role is required to ensure bias and fairness testing has been completed prior to the deployment of an AI solution. Success requires statistical rigor, business context, coding capability, responsible AI awareness, and clear communication.

Responsibilities

• Consulting-Level Advisory Cross-Functional Leadership

  Serve as a trusted advisor on complex HR Transformation Analytics initiatives that span multiple stakeholders, systems, functions, or business outcomes.

  Shape solution direction, operating approach, delivery standards, analytical methods, technical patterns, or governance practices for assigned areas of expertise.

  Lead complex discovery, problem framing, impact analysis, stakeholder alignment, and recommendation development where ownership or solution paths are not yet clear.

  Influence leaders and cross-functional partners through structured analysis, executive-ready communication, trade-off framing, and practical implementation recommendations.

  Establish reusable practices, playbooks, quality standards, templates, and decision frameworks that improve consistency and maturity across the HR Transformation Analytics organization.

• Advanced Analytics Modeling

  Develop statistical, predictive, forecasting, segmentation, classification, natural language, or machine learning analyses for HR and workforce use cases.

  Prepare features, evaluate model performance, document assumptions, and compare modeling approaches based on business needs and data quality.

  Support experiments, pilots, model validations, and analytical prototypes that inform HR decisions and transformation priorities.

• Responsible AI, Evaluation Documentation

  Document model purpose, data sources, assumptions, limitations, performance metrics, risks, and appropriate use cases.

  Support fairness, bias, explainability, privacy, and governance reviews for models or AI-enabled analytical outputs.

  Validate results with business stakeholders and avoid unsupported or overly deterministic interpretations of workforce data.

• Insight Translation Partnership

  Partner with HR domain experts, product analysts, business insights analysts, and data teams to frame problems and interpret results in context.

  Translate advanced analytical findings into practical recommendations, decision options, and measurable next steps.

  Support operationalization of models, model monitoring, and feedback loops in partnership with engineering and governance teams.

  Data storytelling thru compelling executive ready report outs of analytical findings.

• AI-Augmented Data Science

  Use approved AI tools to support code drafting, exploratory analysis, feature brainstorming, documentation, model comparison summaries, and research synthesis.

  Validate AI-assisted code, findings, and modeling recommendations through reproducible methods and peer review.

  Identify opportunities where AI, machine learning, or natural language analytics can responsibly improve HR insight and decision support.

• AI Preparedness Expectations

  Shapes responsible AI adoption patterns for assigned domains, including appropriate use cases, controls, human review expectations, validation approaches, and stakeholder readiness.

  Advises leaders and cross-functional partners on how AI can improve productivity, insight generation, engineering, analytics delivery, process maturity, product outcomes, and workforce readiness.

  Establishes or contributes to reusable AI guidance, prompt patterns, quality checks, governance artifacts, and adoption practices that reduce risk and improve consistency.

  Builds advanced capability in AI-enabled operating models, responsible AI governance, AI-assisted analytics and engineering, and future-of-work implications over the next three years.

  Uses AI to accelerate code drafts, research synthesis, feature exploration, and documentation while preserving reproducibility and validation.

  Understands AI risk, model limitations, fairness, explainability, privacy, and appropriate human oversight for workforce-related models.

  Builds capability in responsible AI.

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