Sr Data Scientist
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Before you apply
Salary$119k – $205k
LocationMilwaukee, WI Corporate
Typefull-time
SenioritySenior
Experience5+ yrs
Company size10,000+ people
PostedNov 6, 2026 · 2d ago
Verified live23h ago
At a glanceSummarised by Seekless from the posting.
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
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
Machine Learning - Applies quantitative analysis methods to large datasets and data structures while developing data pipelines and statistical solutions
Advanced degree (Master's or PhD) in a related quantitative field preferred
Skills
Python
SQL
scikit-learn
databricks
AWS
Azure
About the team
As part of the AI organization, our team works together to solve complex problems, explore new ideas, and turn them into meaningful outcomes. We value curiosity, collaboration, and a willingness to take ownership and learn along the way. We care deeply about the quality and impact of our work while fostering an environment that is supportive and approachable. We want people to feel challenged by the work and energized by the team around them.
What You’ll Do
•
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.
What You’ll Bring to the Role
•
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.
Machine Learning- Applies quantitative analysis methods to large datasets and data structures while developing data pipelines and statistical solutions.
Compensation Range:
Pay Range - Start:
$118,960.00
Pay Range - End:
$178,440.00
Geographic Specific Pay Structure:
Structure 110:
$130,880.00 USD - $196,320.00 USD
Structure 115:
$136,800.00 USD - $205,200.00 USD
Legal
We believe in fairness and transparency. It’s why we share the salary range for most of our roles. However, final salaries are based on a number of factors, including the skills and experience of the candidate; the current market; location of the candidate; and other factors uncovered in the hiring process. The standard pay structure is listed but if you’re living in California, New York City or other eligible location, geographic specific pay structures, compensation and benefits could be applicable, click here to learn more.
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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.