Data Scientist Manager
About The Position
Ameriprise Financial is hiring a Data Scientist Manager to support Client Analytics. This is a senior individual contributor role with technical and analytical leadership responsibilities. The role influences client and advisor strategy and supports deploying AI at scale. It acts as a technical leader and trusted partner to business teams, owning complex problems from solution design through production deployment and translating solutions into actionable insights and data-driven recommendations. The position designs and implements AI and machine learning approaches for client and advisor analytics, including statistical analysis, forecasting, predictive modeling, machine learning, and generative AI. It supports the complete solution lifecycle, leads analytics strategy and ongoing improvements to a field-facing recommendation engine, and ensures solutions meet data, model, and AI governance standards. Employees work in the office at least four days per week, with flexibility to work from home one day per week. Some roles may have additional or different in-office expectations, which will be discussed during hiring.
Requirements
- Master’s degree in a quantitative discipline such as Data Science, Statistics, Computer Science, Mathematics, Economics, or a related field.
- 3–5 years of relevant experience delivering advanced analytics, machine learning, or applied AI solutions.
- Strong foundation in statistics, predictive modeling, and machine learning.
- Experience with modern data science tools, data visualization tools, and programming languages such as Python and SQL, and with ML frameworks.
- Experience working in cloud-based platforms, preferably AWS or Snowflake.
- Ability to independently own complex problems and deliver production-ready solutions.
- Ability to present and communicate complex technical material to facilitate decision-making and drive outcomes, including communicating with less technical partners.
- Ability to work effectively in a collaborative team environment and support multiple projects at one time.
Nice To Haves
- Experience building solutions using LLMs, Agentic AI, RAG, and traditional AI techniques.
- Familiarity with MLOps practices, CI/CD for models, and production monitoring.
- Experience using large-scale data sets.
- Familiarity with financial advisor, client, or customer analytics.
- Experience working in financial services or other highly regulated industries.
Responsibilities
- Design and implement AI and machine learning solutions for complex business problems across client and advisor analytics, using statistical analysis, forecasting, predictive modeling, machine learning, and GenAI.
- Partner with business stakeholders to turn ambiguous needs into analytical approaches, frame problems, define success measures, develop hypotheses, and communicate actionable insights and recommendations.
- Support the full lifecycle of data science and AI solutions, from problem framing, data exploration, and feature engineering through model development, validation, deployment, monitoring, and continuous improvement.
- Lead analytics strategy and ongoing enhancement of the field-facing recommendation engine in partnership with product and technology teams; develop, maintain, monitor, and improve solutions that support personalization, prioritization, and business decision-making.
- Apply enterprise policies, responsible AI practices, validation expectations, documentation standards, and ongoing performance monitoring to meet data, model, and AI governance standards.
- Advance data science best practices, identify opportunities for scalable and automated solutions, mentor peers through knowledge sharing, and stay current on emerging AI, machine learning, and analytics techniques.
Benefits
- Vacation time
- Sick time
- 401(k)
- Health insurance
- Dental insurance
- Life insurance
- Variable pay may be available in the form of bonus, commissions, and/or long-term incentives, depending on the role.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
