As a Senior Associate Data Scientist / Applied AI & ML practitioner in WM IR&A Managed Strategies Risk, you build and productionize AI/ML solutions that improve risk transparency, operational efficiency, and decision support. You partner with risk and technology stakeholders to modernize analytics through machine learning, NLP, and GenAI. You help us operate in a controlled risk-governance environment, ensuring solutions are robust and ethical. You will have the opportunity to mentor team members and contribute to best-in-class practices.

Job responsibilities:

• Design, develop, and deploy ML models and AI-enabled analytics for investment risk oversight and decision support
• Build end-to-end solutions including problem framing, data understanding, feature engineering, modeling, evaluation, deployment, and monitoring
• Apply techniques such as classification, regression, clustering, anomaly detection, time-series modeling, NLP, and deep learning
• Develop GenAI solutions for risk workflows including document understanding, summarization, QA, retrieval-augmented generation, and workflow automation
• Implement and evaluate LLM-based systems with attention to quality, groundedness, drift, and operational controls
• Define best practices for prompts, evaluation frameworks, guardrails, and human-in-the-loop patterns for risk functions
• Partner with technology teams to productionize solutions (APIs, batch pipelines, dashboards) following engineering and control standards
• Contribute to model documentation, testing, monitoring, and performance tracking including model risk and audit readiness
• Communicate results clearly through concise write-ups and presentations to senior stakeholders
• Support governance forums, model reviews, audit discussions, and validation/regulatory requests with clear artifacts and explainable methodology

• Mentor junior team members and contribute to team standards for experimentation, reproducibility, and production ML practices

Required qualifications, capabilities, and skills:

• 3 plus years of hands-on experience in data science, applied ML, or applied AI, delivering solutions used by stakeholders
• Strong Python skills and experience with ML/data libraries (pandas, NumPy, scikit-learn, PyTorch/TensorFlow)
• Demonstrated experience with GenAI/LLM-based applications (transformers, embeddings, RAG, evaluation methods, diffusion models, orchestration frameworks such as LangChain/LangGraph or equivalents)
• Experience working with real-world data and building reliable pipelines (data quality checks, reproducibility, versioning)
• Solid understanding of ML fundamentals and Deep Learning concepts including data representation, model selection, cross-validation, metrics, calibration, overfitting, neural network architecture, custom loss functions, and error analysis
• Ability to translate open-ended business questions into structured modeling problems and measurable outcomes

• Strong communication skills to explain modeling tradeoffs and results to technical and non-technical stakeholders

Preferred qualifications, capabilities, and skills:

• Experience with risk oversight for managed strategies across Private Bank and Consumer Bank businesses
• Familiarity with proprietary and third-party investment products such as registered funds, ETFs/ETNs, separately managed accounts, hedge funds, private equity, and real estate funds
• Experience with responsible AI concepts (bias testing, explainability/interpretability, model governance).
• Experience in financial services, risk, or regulated environments
• Ability to define best practices for prompts, evaluation frameworks, and guardrails
• Experience with visualization and stakeholder tools (Tableau, Power BI, Plotly, Dash, Streamlit) and partnering with technology teams to implement solutions in production
• Experience mentoring and uplifting team members