Sr Data Analyst

Target•Minneapolis, MN

20h•Hybrid

About The Position

The Assets Protection Data Insights team rapidly develops and deploys new innovations to improve existing processes and enable new capabilities that drive productivity, create a great Guest and Team Member experience. We are looking for exceptional people who are proactive, creative, independent, innovative, practical, and comfortable with ambiguity. Are you a critical thinker who seeks the root cause and can analyze both qualitatively and quantitatively? Are you a creative problem-solver who simplifies problems, quickly identifies solutions, and can build and execute effective plans to address? If so, you will have success on one of our dynamic teams. The Assets Protection Data Insights team leverages business experience, thought leadership, and analytical acumen to elevate problem solving for Stores & Supply Chain. The team provides an ideal opportunity to work within a highly collaborative team managing complex solutions. A role on our team means being central to the innovation within Target Stores. You’ll be working in a dynamic and challenging environment where new technologies are deployed constantly, and processes are evolving quickly as these operations mature. Here, you’ll leverage data, statistics, visualizations and predictions to create actionable insights that deliver value across all Target functions. Our savvy reporting and analytics pros use market-leading tools and data automation to make a positive impact that’s felt across the business. Primary Function: As a Sr. Data Analyst, you’ll be part of a team of analysts supporting Target Stores through active exploration of data, experimentation, and advanced analytics. You will apply statistical methods, A/B testing frameworks, and machine learning techniques to generate actionable business insights—enabling the development of scalable metrics, predictive models, and decision tools. A critical part of your role is designing and evaluating experiments (e.g., A/B and multivariate tests), establishing measurement strategies, validating causal impact, and translating results into clear recommendations for business partners. You will share insights through structured communication with end users, gather feedback, and iterate through test-and-learn cycles in partnership with stakeholders. Along the way, you will apply best practices in analytics and ML development by delivering well-documented, reproducible solutions (including model documentation, assumptions, and performance monitoring) and continuously upskilling on emerging tools and technologies with a continuous improvement mindset. In addition, you will actively participate in team meetings and contribute to agile ceremonies, supporting rapid delivery and continuous refinement of analytical products.

Requirements

Four-year degree or equivalent experience
3+ year as a Data Analyst with strong academic performance in a quantitative field; or strong equivalent experience
Advanced SQL experience writing complex queries
Competent and experienced in leveraging Python and R for analytical workflows
Solid problem solving, analytical skills, data curiosity, data mining, Data creation and consolidation
Support conclusions with a clear, understandable story that leverages descriptive statistics, basic inferential statistics, and data visualizations
Willingness to ask questions about business objectives and the measurement needs for a project workstream, and be able to measure objectives & key results
Excellent communication skills with the ability to speak to both business and technical teams, and translate ideas between them
Knowledge of AB Testing methods, time series, Forecasting models including statistical analysis
Experience in analytics tools such as: SQL, Excel, Hive, Spark, Python, R, Power BI, and/or equivalent technologies

Responsibilities

Perform advanced statistical analysis
Lead A/B testing and experimentation: hypothesis → design → measurement → analysis → decisioning.
Build predictive models that forecast outcomes and optimize store/business performance.
Apply causal inference methods when randomized tests aren’t possible.
Ensure statistical rigor, data quality, peer review, and reproducible workflows.
Evaluate and monitor model performance & stability
Partner with stakeholders to operationalize learnings and scale proven improvements.
Contribute to agile ceremonies and continuous improvement of analytics practices.