Senior - Data scientist Manager

Ameriprise•Minneapolis, MN

10d•Hybrid

About The Position

Ameriprise Financial has an opportunity for a Senior Data Scientist Manager to join the team supporting Operation Analytics. This is a senior individual contributor role with technical and analysis leadership responsibilities. You will play an integral role in influencing Service and operations strategy and deploying AI at scale. This position operates as a technical leader and trusted partner to business teams, owning complex problems end to end from solution design through production deployment and translating these solutions into actionable insights and data-driven recommendations.

Requirements

Master’s degree in quantitative discipline (i.e., Data Science, Statistics, Computer Science, Mathematics, Economics or a related field).
5-7 years of relevant experience delivering advanced analytics, machine learning, or applied AI solutions.
Strong foundation in statistics, predictive modeling, and machine learning.
Proven experience with modern data science tools, data visualization tools, and programming languages (e.g., Python, SQL, ML frameworks).
Experience working in cloud‑based platforms, preferably AWS or Snowflake.
Proven ability to independently own complex problems and deliver production‑ready solutions.
Proven ability to present/communicate complex, technical materials in a way that facilitates decision making and drives outcomes; ability to communicate to 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, Agents, RAG, and more traditional AI techniques.
Familiarity with MLOps practices, CI/CD for models, and production monitoring.
Experience using large-scale data sets.
Familiarity with operations analytics, call analytics.
Experience working in financial services or other highly regulated industries.

Responsibilities

Design and implement AI and machine learning solutions that address complex, real-world business problems across Service and Operation analytics, using statistical analysis, forecasting, predictive modeling, machine learning, and GenAI.
Translate ambiguous business needs into clear analytical approaches by partnering with business stakeholders to 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 the analytics strategy and ongoing enhancement of the field-facing recommendation engine, partnering closely with product and technology teams to develop, maintain, monitor, and improve recommendation solutions that support personalization, prioritization, and business decision-making.
Ensure solutions meet data, model, and AI governance standards by applying enterprise policies, responsible AI practices, validation expectations, documentation standards, and ongoing performance monitoring.
Serve as a technical leader and thought partner by advancing data science best practices, identifying opportunities for scalable and automated solutions, mentoring peers through knowledge sharing, and staying current on emerging AI, machine learning, and analytics techniques.
Deliver analytic strategy that delivers measurable business impact.