Lead Data Scientist

Overview: UP.Labs is a dynamic venture studio building innovative startups from the ground up. We are partnering with Wabash National, one of the largest tractor trailer manufacturers in the country, to develop PartsPulse - an AI-driven analytics platform transforming how OEMs manage and grow their aftermarket parts businesses.
We seek a Lead Data Scientist who excels at translating business questions into rigorous analytical frameworks and building production analytics systems that drive measurable outcomes. This role focuses on deep analytical thinking, causal inference, and creating the intelligence layer that powers autonomous business decisions.
Technical Challenge: Managing an aftermarket parts business with thousands of SKUs, dozens of dealers, and thousands of end customers creates complex planning and operational challenges. You will design and build core analytics and statistical models that power PartsPulse AI-driven decision engine, enabling revenue decomposition, demand forecasting with sparse data, causal inference for measuring intervention incrementality, growth analytics across customer segments, and anomaly detection that distinguishes signal from noise.
In this role you will:Design and implement statistical models for revenue decomposition, attribution, and root cause analysisBuild causal inference frameworks to measure incrementality of pricing, inventory, and promotional interventionsDevelop demand forecasting systems that handle sparse data, seasonality, and structural breaksCreate propensity models for customer behavior, churn prediction, and growth analyticsLead end-to-end analytics projects from problem framing to production deploymentBuild scalable analytics pipelines that operate autonomously on weekly cadenceEstablish metrics frameworks connecting analytics to business outcomesMentor data scientists and analysts, championing best practices for statistical rigor You should have:Masters degree in Statistics, Economics, Mathematics, Computer Science, Operations Research, or related quantitative field5-7 years of experience in data science with proven track record of leading technical projects end-to-endExpert-level knowledge of causal inference: difference-in-differences, matching, regression discontinuity, instrumental variables, synthetic controlsStrong foundation in experimental design, A/B testing, and quasi-experimental methodsDeep experience with time series forecasting and statistical modeling (regression, hierarchical models, regularization)Proficiency in Python (pandas, scikit-learn, statsmodels) and SQL for complex data manipulationExperience in growth analytics: cohort analysis, revenue decomposition, retention modeling, mix-effect analysisStrong business acumen with ability to translate ambiguous problems into analytical frameworksExcellent communication skills - explain complex statistical concepts to non-technical stakeholdersBias toward action - value delivering 80 percent accuracy Monday morning over 99 percent next month
Preferred Expertise:Advanced degree (Masters or PhD) in quantitative fieldExperience with Bayesian modeling (PyMC3, Stan) and structural time series forecastingBackground in supply chain analytics: demand forecasting, inventory optimization, pricing analyticsExperience with Databricks, Spark, or similar big data platformsFamiliarity with LLMs and GenAI for analytics applications
UP.Labs Summary: We build high-growth technology startups that enable faster, cleaner, and safer movement of people and goods. We work with corporate investors over multi-year periods to launch portfolios of mobility-focused ventures, dedicating our team to the first year of a venture life cycle from ideation to MVP build and beyond.

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