As aData Science LeadinWealth Management Decision Sciences, you will lead a team that delivers insights and machine learning solutions that improve client outcomes, product performance, and operational efficiency. You will partner closely with product, strategy, and operations teams to frame problems, define success measures, and deliver clear recommendations to senior stakeholders. You will help the team apply the right level of analytical rigorranging from descriptive analytics and visualization to advanced modelingbased on the business need. You will also identify and develop high-value opportunities to leverage artificial intelligence and agentic tools to elevate analytical productivity and decision quality. This is a vital hands-on role and manager role so we
9re looking for exceptionally creative, ambitious, "data curious" thinkers who can help extract true business value from data.

Job responsibilities

• Collaborate with product, strategy, and operations teams to provide analytical support for wealth management initiatives and decision-making
• Apply statistical methods, data analysis, and artificial intelligence-assisted approaches to solve business problems across product, financial, client engagement, and operational outcomes
• Lead end-to-end analytics delivery, from problem framing and measurement design through insight generation and communication to senior stakeholders
• Develop and refine analytics approaches for machine learning and trigger capabilities thatimprove the operations process in a scalable, sustainable way
• Analyze client usage across risk segments and products to understand relationships between risk appetite and portfolio allocations
• Operationalize experimentation frameworks to evaluate enhancements, quantify impact, and inform prioritization
• Assess client experience and journey outcomes, including artificial intelligence-enabled discovery, to identify improvement opportunities and measurable value creation
• Identify and develop high-value artificial intelligence and agentic use cases that improve analytical productivity, decision quality, portfolio allocations, and customer outcomes

Required qualifications, capabilities, and skills

• BS Degree in an applicable STEM field and 5+ years of industry experience producing advanced analytics work.

• 2 yearsof people management experience, including performance management, coaching, and team development
• Demonstrated experience leading teams of data scientists to deliver analytics and machine learning solutions that improve business performance
• Ability to right-size analytical approaches to problem complexity, from descriptive analytics and visualization through machine learning and artificial intelligence-based decisioning
• Strong business acumen and critical thinking, with the ability to translate ambiguous questions into well-defined analytical problem statements and success metrics
• Hands-on experience applying statistical and quantitative techniques (for example: decision trees, linear regression, logistic regression, ridge regression, multicollinearity analysis, string similarity, behavioral analytics, and look-alike modeling)
• Effectively coach a team on both the computational aspects of big data as well as working with statistical models (e.g., complex SQL scripts, PySpark libraries or Jupyter Notebooks) while staying focused on solving problems, not just boosting model performance curves
• Know when the juice is worth the squeeze 6 you stay abreast of new applications in machine learning, deep learning, and AI but can recognize and apply the right analytic approach when the challenge you are solving is less complex
• Strong communication skills, with experience synthesizing insights and influencing stakeholders through clear recommendations and executive-ready storytelling

Preferred qualifications, capabilities, and skills

• Experience supporting wealth management, investing, or self-directed investing businesses
• Demonstrated growth mindset through ongoing learning (for example: industry podcasts, staying current on new methods, or participation in data science competitions and conferences such as Knowledge Discovery and Data Mining)