Lead AI and Data Science Engineer II

Lead AI and Data Science Engineer II

Drive the design and delivery of advanced analytics, artificial intelligence (AI), and generative artificial intelligence (GenAI) solutions that inform Talent strategy and workforce decisions. In this role, you will lead complex data science work that combines research design, statistical analysis, machine learning, and application development to solve high-priority people challenges. You will also partner with business, Talent, and technology stakeholders to translate workforce data into actionable insights, tools, and recommendations.

Recruiting for this role ends on August 10, 2026.

In this role, you will lead the data science dimension of that work, shaping research design, analytical methodology, and solution development while helping develop junior data scientists across the full analytics lifecycle.

• Partner with the Advanced Analytics & AI leader to help shape and execute the People Analytics portfolio, using data-driven insights to inform Talent strategy, establish priorities, and manage multiple initiatives.
• Collaborate with stakeholders across Talent, business leadership, and ITS to define business needs, identify analytics opportunities, communicate complex technical concepts, and advise on the benefits and limitations of automation and artificial intelligence (AI).
• Develop and drive strategy to understand and improve data quality across relevant Talent data assets.

• Design and structure analytical work, including research design, project planning, and use case development, and apply advanced statistical and machine learning methods to generate rigorous, actionable insights.
• Perform analytics in cloud-based environments, support the development of clear leadership-ready presentations, and stay current on developments in data science, behavioral science, and adjacent disciplines.

• Develop full-stack, web-based data and generative artificial intelligence (GenAI) applications that improve Talent reporting and business operations.
• Partner with data engineering teams on pipeline architecture and infrastructure, using knowledge of extract, transform, load (ETL), version control, and continuous integration and continuous delivery (CI/CD) concepts to inform technical decisions.

• Mentor junior and mid-level data scientists across the analytics lifecycle, from problem framing and data collection through analysis and synthesis of findings.
• Provide structure and oversight for analytically complex projects, while assessing talent, delivering developmental feedback, and contributing to a high-performing analytics team.

• Ability to work independently and collaborate as part of a team
• Effective written and verbal communication skills
• Meticulous attention to detail and quality of work product
• Ability to build and sustain professional relationships
• Ability to lead projects or work streams
• Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment
• Strong interpersonal skills and professional demeanor
• Ability to meet deadlines
• Ability to mentor and provide clear guidance to others

The Talent Experience & Engagement, People Analytics - Advanced Analytics & AI team helps the firm make informed people and business decisions by translating workforce data into actionable insights, tools, and strategies. The team combines behavioral science, organizational research, advanced analytics, and artificial intelligence (AI) to address leadership questions, improve decision-making, and create scalable solutions with measurable impact.

The work is delivered through two connected capabilities. Organizational research and storytelling ground analyses in rigorous social science and clear, decision-oriented narratives. Data science applies statistical analysis, machine learning, and AI to identify meaningful patterns in workforce data, build data and generative artificial intelligence (GenAI) applications, and partner with Talent data engineering teams on pipelines and infrastructure.

Required:

• Graduate degree in Applied Statistics, Computer Science, Life Sciences, Industrial-Organizational Psychology, Organizational Behavior, Sociology, Economics, Anthropology, or another quantitative discipline
• 6+ years of experience in data science, analytics, or applied research
• Experience using Python and Structured Query Language (SQL), with working knowledge of JavaScript, Type Script, Hyper Text Markup Language (HTML), and Cascading Style Sheets (CSS)
• Experience applying multivariate statistics and machine learning methods, including regression, structural equation modeling, factor analysis, decision trees, clustering, and dimension reduction, in Apache Spark or Databricks environments
• Experience developing artificial intelligence (AI) or generative artificial intelligence (GenAI) applications and working with extract,…