Principal Specialist, Data Science & Analytics

ROLE PROFILE

Why This Role Matters

This role leads advanced data science and analytics initiatives within the Agentic AI department, translating business problems into scalable analytical solutions, AI-enabled insights, predictive models, and decision intelligence products. It helps build enterprise capabilities that use trusted data, machine learning, automation, and agentic AI concepts to improve productivity, operational performance, and business decision-making.

What You Will Deliver

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.

What Success Looks Like

• Business problems are translated into clear data science use cases, analytical approaches, and measurable value outcomes.
• Predictive, prescriptive, and decision intelligence solutions are delivered with strong technical quality and practical business relevance.
• Agentic AI use cases are supported with trusted data, robust models, evaluation methods, controls, and human oversight where required.
• Analytics and AI products are reusable, documented, governed, monitored, and aligned with responsible AI expectations.

• Stakeholders receive clear insights, recommendations, and decision support that improve productivity, performance, and adoption.

   

  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