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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in leading multidisciplinary teams in data science and machine learning, with a strong focus on developing and implementing AI strategies, evaluation frameworks, and responsible AI governance in a regulated environment. Proficient in managing the end-to-end lifecycle of AI models and systems, ensuring data quality and compliance while driving innovation in wealth management.
Highest-signal resume keywords
Data Science LeadershipLLM-Based Systems DesignQuantitative Evaluation FrameworksStatistical Inference ExpertiseResponsible AI Governance
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
PythonSQLMachine Learning MethodsStatistical InferenceModel Risk ManagementEvaluation FrameworksData TransformationGenerative AIMulti-Agent OrchestrationCausal Inference
Soft Skills
People LeadershipStakeholder EngagementCommunication
Tools & Technologies
MLOpsLLMOpsCI/CDVersioningObservabilityIncident Response
Industry Keywords
Wealth ManagementFinancial PlanningManaged AdvicePortfolio ConstructionGoals-Based Planning
Tech Stack
Tools & technologiesPythonSQL
About the role
Key responsibilities & impact- Lead a multidisciplinary team of data scientists, ML engineers and applied AI practitioners
- Own the end-to-end lifecycle from problem framing and hypothesis design through model and agent development, evaluation, deployment, monitoring and decommissioning
- Build, develop, evaluate and supervise team members across data science, ML engineering and applied AI disciplines
- Establish upskilling in generative AI, prompt and context engineering, agentic patterns, retrieval architectures and evaluation methodology
- Translate enterprise AI and data science strategy into a prioritized tactical roadmap for Advice and Wealth Management
- Direct the design of multi-agent and orchestrated systems, including planner/executor patterns, tool and function calling, memory and state management, retrieval-augmented generation, human-in-the-loop checkpoints and deterministic fallback paths
- Evaluate and select models; own build-versus-buy-versus-tune decisions
- Manage token economy, inference capacity, quotas, unit economics and ROI
- Build and maintain evaluation harnesses for accuracy, groundedness, hallucination rate, tone, refusal behavior, latency and regression
- Oversee analytics portfolio spanning statistical and machine learning models, causal inference, experimentation, forecasting, generative applications and agentic applications
- Lead model, methodology, data, prompt, agent trajectory and output validation
- Govern data quality, provenance, unstructured corpora and retrieval sources
- Partner with engineering on MLOps and LLMOps, including CI/CD, versioning, observability, tracing, guardrails, incident response and rollback
- Implement responsible AI governance across model and agent lifecycles
- Partner with Legal, Compliance, Risk and Model Risk Management in the regulated advice environment
- Evaluate emerging technologies and manage AI vendors and model providers
- Engage stakeholders, translate requirements into analytic or AI approaches, and represent the domain to senior leadership
- Participate in special projects and perform other duties as assigned
Requirements
What you’ll need- Minimum ten years of related work experience in analytical roles, including demonstrated people leadership
- Hands-on experience designing, evaluating and productionizing LLM-based systems, including retrieval-augmented generation, tool and function calling, multi-agent orchestration, and guardrail implementation
- Proven experience building quantitative evaluation frameworks for non-deterministic systems, including rubric design, benchmark construction and regression testing
- Strong programming skills for accessing, transforming and preparing large-scale structured and unstructured data for modeling
- Python required
- SQL and modern data platform experience expected
- Deep applied command of statistical inference, experimentation and machine learning methods
- Working knowledge of model risk management, responsible AI frameworks and controls in a regulated environment
- Experience in advice, wealth management, or financial planning, including familiarity with managed advice, portfolio construction, goals-based planning, advisor workflows, client segmentation, or retirement outcomes
- Undergraduate degree in Analytics, Applied Mathematics, Computer Science, Economics, Statistics or a related analytical field, or an equivalent combination of training and experience
- No visa sponsorship is offered for this position
Benefits
Comp & perks- Hybrid working model with enhanced flexibility, in-person learning, collaboration, and connection
- Employee learning and development opportunities
- Compensation decisions in accordance with applicable Human Resources policies and procedures