Job Summary:

As aData Strategist LeadinPrincipal Investment Risk Management, you will define and execute the risk data strategy and governance model while partnering across lines of business, functional stakeholders, and technology teams to deliver trusted, decision-grade data products.

You will lead applied AI/Machine Learning delivery1 spanning generative AI, agentic workflows, and traditional Machine Learning1 to improve risk oversight, analytics, metrics, and reporting. You will operate with strong ownership from requirements through scaled adoption, ensuring verification, validation, and guardrails that produce safe, reliable outputs in production.

Job Responsibilities:

• OwnPrincipal Risks data foundations including controlled sourcing/integration, metadata/catalog, lineage, and lifecycle risk controls (protection, retention/destruction, storage, usage, quality).
• Defineand evolve data governance standards, publishing patterns, and documentation expectations to enable trusted consumption and self-service.
• Deliverrisk data products that support business operations, strategic objectives, analytics, metrics, and reporting across the principal investment process.
• Establishmeasurable data KPIs (e.g., quality, timeliness, completeness, lineage coverage, control adherence) and use them to steer roadmap and prioritization.
• Designapplied AI/ML solutions (generative AI, agentic workflows, traditional ML) to address Principal Risk analytics and oversight use cases.
• ImplementLLM agents and multi-agent systems including planning, parallel task execution, entity resolution, and human-in-the-loop escalation.
• Translatebusiness needs into delivery artifacts including technical designs, acceptance criteria, measurable outcomes, and execution plans.
• Leadend-to-end delivery from requirements  POC  production  scaled adoption, including operating model handoff where needed.
• Buildverification and validation mechanisms such as business-rule checks, evaluation datasets, and regression testing to ensure safe and reliable outputs.
• Operateproduction solutions through monitoring, performance/stability improvements, drift management, and continuous iteration.
• Partnerwith technology teams to document data sources, formats, and flows while implementing validation to ensure downstream readiness for analytics and reporting.
  

Required Qualifications, Capabilities, and Skills:

• Bachelors degree (or equivalent experience) in a relevant field (e.g., data science, computer science, engineering, math, sciences) or equivalent professional experience.
• 5+ years of experience in data management, data governance, risk management/analytics, data science, or a closely related domain.
• Demonstratestrong analytical problem-solving skills with the ability to execute effectively in time-sensitive environments.
• Communicateclearly in writing and verbally, producing high-quality documentation and influencing business, risk, and technology stakeholders.
• Applyfoundational knowledge of data management principles and end-to-end data lifecycle management.
• Programeffectively in Python and work confidently with SQL (or similar querying languages).
• Buildand interpret dashboards/analysis using Tableau (or equivalent BI experience aligned to the roles needs).
• Collaboratecross-functionally to define requirements, align stakeholders, and drive approvals for delivery and adoption.
• Executedelivery with accountability, including defining outcomes, tracking progress, and managing dependencies through to production.
• Promotestrong data quality and control discipline, including validation and readiness for downstream reporting and analytics.
• Adhereto data protection and usage expectations appropriate for risk data and decisioning workflows.
  

Preferred Qualifications, Capabilities, and Skills:

• Deliverhands-on experience with an LLM platform (model onboarding/serving, prompt/version management, evaluations).
• Buildexperience with agent orchestration frameworks (tool use, retrieval-augmented generation, state/context management).
• Operationalizeproduction systems with observability, alerting, dashboards, runbooks, and post-deploy monitoring/continuous improvement.
• Usefamiliarity with data governance tooling for catalog/metadata/lineage and modern data publishing standards.
• Leveragefamiliarity with big data platforms, data architecture patterns, and governance tools/platforms.
• Applycloud/DevOps exposure (e.g., AWS, Linux, Git) and observability tooling experience to support reliable delivery.
• Demonstratea track record of measurable improvements through automation, operational rigor, and end-to-end data lifecycle initiatives (onboarding, integrity checks, archiving, migration/decommissioning).