Quant Analytics, Senior Associate - Asset Wealth Management
Key Skills for This Role
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Job responsibilities
- Own the Client Analytics Data Product roadmap, success measures, and delivery plan, prioritizing work by business use case and adoption outcomes.
- Partner with marketing, sales, finance, product strategy, client service, and investment teams to run intake, align priorities, and drive adoption of analytics products.
- Deliver quantitative insights and senior-ready recommendations that improve decisions across the business (for example, segmentation, coverage strategy, profitability, retention, and flows).
- Design and execute analyses using methods such as regression, segmentation, propensity modeling, attrition modeling, forecasting, attribution, and test-and-learn experimentation.
- Define shared client and product definitions and certified metrics so teams and AI tools consistently get the same answer to the same question.
- Build scalable dashboards and reusable analytics products in Microsoft Power BI and Qlik Sense, and partner with technology teams to embed insights into workflows and reporting.
- Integrate internal and third-party data and build validated analytical models in SQL and Python, addressing entity resolution, hierarchies, history, granularity, and data quality.
- 3+ years of experience in quantitative analytics, data science, business intelligence, or analytics consulting delivering insights and analytics products for multiple business teams.
- Demonstrated quantitative skills in statistics, regression, segmentation or clustering, forecasting, and experimental design.
- Advanced SQL and Python experience (for example, pandas, scikit-learn, statsmodels) working with large, multi-source datasets.
- Hands-on experience building dashboards and analytics products using Microsoft Power BI and/or Qlik Sense.
- Proven ability to translate analysis into clear, actionable recommendations for senior stakeholders, supported by concrete examples and outcomes.
- Strong stakeholder management skills, including influencing without direct authority, aligning competing priorities, and driving adoption across teams.
- Working knowledge of data governance fundamentals (definitions, lineage, data quality, access controls) and how trusted data supports responsible AI use cases.
- Experience using third-party asset management datasets such as Morningstar, FINTRX, Broadridge, and/or Preqin for competitive and industry benchmarking.
- Knowledge of asset and wealth management products and distribution, including mutual funds, exchange-traded funds, separately managed accounts, alternatives, assets under management, and flows.
- Experience applying advanced methods such as propensity modeling, next-best-action modeling, causal inference, and/or time-series forecasting to client and product datasets.
- Advanced dashboarding and semantic modeling skills (for example, Data Analysis Expressions, Power Query, and Qlik scripting and set analysis), with familiarity in Snowflake, Databricks, or dbt as a plus.
Required qualifications, capabilities and skills
- 3+ years of experience in quantitative analytics, data science, business intelligence, or analytics consulting delivering insights and analytics products for multiple business teams.
- Demonstrated quantitative skills in statistics, regression, segmentation or clustering, forecasting, and experimental design.
- Advanced SQL and Python experience (for example, pandas, scikit-learn, statsmodels) working with large, multi-source datasets.
- Hands-on experience building dashboards and analytics products using Microsoft Power BI and/or Qlik Sense.
- Proven ability to translate analysis into clear, actionable recommendations for senior stakeholders, supported by concrete examples and outcomes.
- Strong stakeholder management skills, including influencing without direct authority, aligning competing priorities, and driving adoption across teams.
- Working knowledge of data governance fundamentals (definitions, lineage, data quality, access controls) and how trusted data supports responsible AI use cases.
Preferred qualifications, capabilities and skills
- Experience using third-party asset management datasets such as Morningstar, FINTRX, Broadridge, and/or Preqin for competitive and industry benchmarking.
- Knowledge of asset and wealth management products and distribution, including mutual funds, exchange-traded funds, separately managed accounts, alternatives, assets under management, and flows.
- Experience applying advanced methods such as propensity modeling, next-best-action modeling, causal inference, and/or time-series forecasting to client and product datasets.
- Advanced dashboarding and semantic modeling skills (for example, Data Analysis Expressions, Power Query, and Qlik scripting and set analysis), with familiarity in Snowflake, Databricks, or dbt as a plus.
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