Data Scientist
HighbeamBanking for company
New York, United StatesSenior
Acrew Capital
Two Sigma Ventures
Vesey Ventures
FirstMark
Mayfield
GSF India
Data & AI
About the role
TL;DR
Data Scientist to analyze customer health and behaviors for ecommerce brands.
- •We are looking for an experienced data scientist to help build the analytical engine behind how we understand customer health, behaviors, product usage, success patterns, and risks across our portfolio of ecommerce brands.
- •Key Responsibilities Analyze customer-level financial health, including revenue consistency, margin structure, cashflow, marketing efficiency, inventory cycles, capital usage, and risk indicators Create customer health scores, segmentations, and heuristics that the entire company can rely on Conduct quantitative research across the entire platform and market sector to understand themes in e-commerce Partner with CS, GTM, and Capital teams to analyze behavior across segments, identify high-value opportunities, and explain customer challenges Inform underwriting models with customer behavior and financial patterns Requirements 5+ years experience in customer analytics, data science, financial analysis, or a related quantitative field Strong background in statistics, machine learning, computer science, or quantitative discipline, especially with large data sets Experience analyzing customer behavior, product usage, lifecycle metrics, or financial/transactional datasets Ability to synthesize complex data into clear insights and compelling narratives for non-technical audiences Familiarity with e-commerce fundamentals (conversion, CAC, LTV, margin structure, inventory cycles) or strong willingness and ability to become an expert quickly
Required skills
PythonSQLPandasNumPyscikit-learnTableauPower BILookerFinancial Analysis
Nice-to-have skills
A/B TestingProduct AnalyticsFP&A
Domain expertise
fintechecommerce
Benefits & perks
Competitive salary, Equity, Generous PTO, Comprehensive health & benefits package, Stipends for lunch, commute, wellness, and purchasing customer products, Yearly offsite
Tech stack
PythonSQLPandasNumPyscikit-learnTableauPower BILooker