Sr Data Scientist

• Applies and integrates advanced statistical, mathematical, data mining, predictive modeling and business analysis skills and techniques to discover and identify new insights, predict and prescribe outcomes, advise on turning new insights into actionable opportunities and outcomes, and provide the tracking and measurement of results.
• Develop LLM-based, mathematical models, algorithms, machine learning techniques, and robust analytics to support analytic insights and visualization of complex data sets for productization and monetization.
• Create evaluation frameworks and monitor deployed models and AI systems for performance, drift and unintended outcomes.
• Applies creative problem solving and inter-departmental networking to address data and information needs across the enterprise.
• Shares best practices with peers through coaching and mentoring core methodologies, patterns, standards and processes.
• Through a clear understanding of the assigned business area, utilizes technical skills to access and extract data from diverse sources residing on multiple platforms and implement large, complex data models and information solutions by combining, synthesizing and structuring data from various data sources.

• Proficiency in Python and SQL; experience with common ML libraries (e.g. scikit-learn) and cloud data platforms (e.g. databricks)
• Experience with agentic engineering: designing agents and multi-agent workflows, including tool integration, orchestration, guardrails and human-in-the-loop checkpoints
• Understanding of context engineering. How to structure instructions, retrieved information, tool outputs, and memory so AI systems perform reliably
• Understanding of how to evaluate AI systems, including accuracy, hallucination rates, consistency, and cost/latency, using structured test sets and human review
• Ability and desire to mentor other team members through projects independently from start to finish, working with internal and external teams to make decisions and maintain momentum
• Excellent time and project management skills, with the ability to manage detailed work and communicate project status effectively to all levels
• Proven track record in meeting aggressive deadlines
• At least 5 years of relevant experience with demonstrated success using data science to deliver significant business impact or develop data- or algorithm-based products.
• Bachelor’s degree in Data Science, Statistics, Mathematics, Operations Research, Computer Science, Econometrics, or a related quantitative field.
• Advanced degree (Master’s or PhD) in a related quantitative field preferred.

Skills You’ll Have

Cloud Computing- Applies cloud architecture principles across AWS and Azure platforms to design, plan, and scale cloud implementations and initiatives.

Industry Knowledge- Applies knowledge of industry practices, regulatory considerations, market trends, and the competitive landscape to understand and address business needs and challenges.

Python or R - Uses Python or R to develop logical code for small- and large-scale projects and perform statistical analysis and graphics.

Statistical Analysis- Uses quantitative and computational reasoning to analyze data, draw practical conclusions for business challenges, and apply statistical probabilities and trends to predict outcomes.

ML Ops- Deploys and maintains machine-learning models in production reliably and efficiently to improve workflow efficiency.

Northwestern Mutual is an equal opportunity employer that welcomes talented individuals of all backgrounds. We are committed to creating and maintaining an environment in which each employee can contribute creative ideas, seek challenges, assume leadership and continue to focus on meeting and exceeding business and personal objectives.