Principal Specialist, Data Science & Artificial Intelligence

Job description

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 using trusted data, machine learning, automation, and agentic AI concepts to improve productivity, operational performance, and business decision‑making. Responsibilities include leading data science and analytics solutions, designing predictive, prescriptive, diagnostic, and exploratory analytics, developing machine learning models and advanced analytics products, ensuring outputs are interpretable and actionable, enabling agentic AI use cases, collaborating with AI engineers and data teams, defining evaluation approaches, promoting responsible AI, creating reusable analytical assets, partnering with data platform and governance teams, integrating analytics into dashboards and applications, maintaining documentation, applying governance practices, defining success metrics, monitoring performance post‑deployment, providing technical leadership and stakeholder advisory, and communicating insights clearly.

Requirements

• Bachelor’s Degree in Data Science, Computer Science, Artificial Intelligence, Statistics, Mathematics, Engineering, Information Systems, or related field
• Master’s Degree preferred
• 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 preferred
• proficiency in Python, SQL, analytics platforms, notebooks, visualization tools, cloud data platforms, and MLOps practices
• experience in data preparation, feature engineering, model validation, performance monitoring, drift detection, and lifecycle management
• knowledge of responsible AI, explainability, transparency, bias awareness, human oversight, and governance controls
• ability to think in data products, create reusable analytical assets, document, scale, and operationalize them
• skills in 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

Skills