Top Entry Level Data Science Jobs in Minneapolis, MN

Build predictive and machine learning models for patient needs, operational forecasting, churn, advocate quality, and risk. Design A/B tests and causal inference studies, apply advanced statistical methods, productionize models with data engineers, and communicate insights to business stakeholders. The role requires Python, advanced SQL, forecasting expertise, statistical proficiency, data privacy awareness, and the ability to work independently in a fast-paced healthcare startup.

Develop measurement frameworks for offline and out-of-home marketing campaigns, including geo holdout tests, matched-market tests, media mix modeling, and incrementality studies. Combine offline spending with digital and product signals to assess marketing effectiveness. Partner with Marketing leadership and Growth Data scientists to select test markets and channels, size investments, align online and offline measurement, and communicate uncertainty clearly.

Own product analytics for ElevenLabs products, defining health metrics across activation, adoption, retention, and engagement. Design and analyze A/B tests, feature launches, and onboarding changes. Partner with Product, Growth, Analytics Engineering, and Data teams to convert ambiguous questions into trusted analysis and certified KPI layers. Apply SQL, Python, experimentation, and product analytics expertise to support growth and product decisions.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

Partner with AI labs to improve frontier models by designing targeted data collection workflows, analyzing experimental results, and implementing quality controls. The role involves evaluating model behavior, reducing bias, supporting model safety and alignment, and translating technical findings into actionable insights for AI researchers and customers. Python is used for data analysis, pipeline development, and workflow prototyping.

Own Alpaca’s growth data strategy across acquisition, activation, retention, referral, and monetization. Build experimentation frameworks, causal and incrementality analyses, funnel and cohort models, LTV/CAC and churn forecasts, marketing measurement, governed growth metrics, and self-serve analytics. Partner with Product, Marketing, Sales, Engineering, and Finance to influence strategy and roadmaps, communicate insights to leadership, mentor data professionals, and establish standards for growth analytics.

Prototype and validate analytical methods and LLM applications for political, media, and advertising data. Build proof-of-concept tools, datasets, pipelines, and applications, then engineer validated methods toward production. Apply statistical and data science techniques, maintain rigorous software development practices, communicate findings clearly, and collaborate with technical and non-technical stakeholders.

Assist senior data scientists with data collection, preprocessing, cleaning, and basic statistical analysis. Support model development, perform basic visualizations, and document processes and findings while learning and applying data science techniques.

Leads data science and reporting initiatives for a commercial insurance organization, including master data management, dashboard and scorecard development, reporting-environment design, data warehouse support, data integrity, normalization, data cleansing, troubleshooting, and end-user training.

Own end-to-end data science for emerging content surfaces, including product experimentation, causal inference, exploratory analysis, metrics, dashboards, and strategic insights. Identify opportunities to grow content supply, creators, and engagement while improving discovery. Lead content data architecture and quality initiatives, develop source-of-truth assets, and apply advanced statistical methods such as quasi-experiments and network testing. Partner with Product, Engineering, Design, and Research to influence product direction and communicate findings to diverse audiences.

Partner with product engineering to design and analyze experiments, apply causal reasoning and statistical modeling to marketplace data, and drive data-informed product decisions across the engineering org.