Principal Specialist, Data Science & Artificial Intelligence

Why This Role Matters

Lead Data Science and Analytics Solutions

• Translate business priorities, operational challenges, and use cases into clear data science problem statements, analytical approaches, and measurable outcomes.
• Design and deliver predictive, prescriptive, diagnostic, and exploratory analytics solutions that support decision-making across business and operational domains.
• Develop machine learning models, statistical analyses, simulations, optimization approaches, and advanced analytics products using enterprise data assets.
• Ensure analytical outputs are interpretable, actionable, repeatable, and aligned with business value, adoption needs, and governance requirements.

Enable Agentic AI and Intelligent Analytics

• Support the development of agentic AI use cases by defining data, model, workflow, evaluation, and decision-support requirements.
• Collaborate with AI engineers, platform teams, data engineers, and business stakeholders to embed analytics and machine learning into autonomous or semi-autonomous workflows.
• Define evaluation approaches for AI-enabled insights, model performance, business impact, human oversight, and continuous improvement.
• Promote responsible use of agentic AI by ensuring transparency, explainability, control, auditability, and fit-for-purpose model deployment.

Advance Data Products and Decision Intelligence

• Create reusable analytical assets, data science patterns, feature logic, model components, and decision intelligence products that can scale across business domains.
• Partner with data platform, governance, and architecture teams to ensure data products are trusted, well-documented, reusable, and operationally maintainable.
• Integrate analytics into dashboards, workflows, applications, copilots, and AI agents where decision support can improve speed, quality, and productivity.
• Maintain clear documentation covering assumptions, data sources, model logic, limitations, controls, and expected business usage.

Ensure Model Quality, Governance and Value Realization

• Apply appropriate data science governance practices for model validation, performance monitoring, bias checks, versioning, deployment readiness, and lifecycle management.
• Define success measures, impact metrics, adoption criteria, and benefit tracking approaches for analytics and AI-enabled solutions.
• Monitor solution performance after deployment and recommend improvements based on usage, feedback, model drift, data quality, and business impact.
• Ensure data science and analytics solutions remain compliant with data governance, cybersecurity, privacy, and responsible AI expectations.

Provide Technical Leadership and Stakeholder Advisory

• Provide expert guidance to analysts, data scientists, engineers, and business teams on analytical methods, model design, evaluation, and adoption.
• Advise stakeholders on what analytics or AI can realistically solve, required data readiness, implementation complexity, and expected value.
• Review analytical designs, model outputs, assumptions, and recommendations to ensure technical quality and business relevance.
• Communicate insights, risks, limitations, and recommended actions clearly to technical, operational, and executive stakeholders.

Minimum Qualifications

• Bachelor's Degree in Data Science, Computer Science, Artificial Intelligence, Statistics, Mathematics, Engineering, Information Systems, or a related discipline.
• Master's Degree is preferred.

Experience

• Minimum 10 years of experience in data science, advanced analytics, machine learning, AI solution delivery, decision intelligence, or data product development.
• Strong experience translating business requirements into analytical solutions, developing models, evaluating performance, and deploying analytics into operational workflows.
• Experience with agentic AI, generative AI, MLOps, responsible AI, cloud data platforms, or enterprise analytics environments is preferred.

Skills That Matter

• Advanced data science, machine learning, statistical modeling, optimization, simulation, and predictive analytics.
• Agentic AI concepts, generative AI, decision intelligence, AI workflow design, and model evaluation.
• Python, SQL, analytics platforms, notebooks, visualization tools, cloud data platforms, and MLOps practices.
• Data preparation, feature engineering, model validation, performance monitoring, drift detection, and lifecycle management.
• Responsible AI, explainability, transparency, bias awareness, human oversight, and governance controls.
• Data product thinking, reusable analytical assets, documentation, scalability, and operationalization.
• Business problem framing, stakeholder advisory, insight storytelling, and value realization tracking.
• Ability to coach teams, review analytical work, manage ambiguity, and communicate with technical and executive audiences.