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Shiftsmart is building a labor platform that pairs workforce management technology with a network of 5M flexible workers, serving major companies like Circle K, Pepsi, Dollar General, and Starbucks. The company has grown 2-3x annually and raised $120M+ from top-tier investors including D1 Capital and Imaginary Ventures.
The Supply team manages partner (worker) supply to ensure sufficient high-quality workers are available to fill shifts for customers. As a Data Analyst on this team, you will build and maintain core data tables and models across partner success, marketplace, and supply functions to drive fill rate and fulfillment quality. You will be instrumental in designing new business processes for scale and optimizing existing operations.
Key responsibilities include:
- Scaling the Supply Model: Expand the existing supply model by building core DBT data tables that dynamically support decisions across the Partner organization, turning data models into foundational infrastructure that allows the business to respond to real-world outcomes with less manual intervention.
- Investigating and Diagnosing Data Anomalies: Trace issues through the data pipeline to identify root causes and determine appropriate solutions when business performance looks unexpected.
- Operational Analytics: Build and maintain BigQuery analyses and core data tables supporting day-to-day operational decisions, including zone-level health dashboards, partner funnel breakdowns, fill-rate trend detection, cohort performance reporting, and related analyses.
- Maintenance and Reliability: Own the maintenance, development, and expansion of core data tables in DBT across the Supply team. Build sustainable, reusable data models that reliably power operational decisions across Supply, Marketplace, and Partner Success. Ensure recurring analyses and models remain accurate and up to date as the business evolves.
- Predictive Analytics: Develop and iterate on predictive models to help the business proactively identify and address operational risks, including feature engineering, backtesting, model evaluation, and communicating results to stakeholders.
- Cross-Functional Collaboration: Partner with Supply, Marketplace, Partner Success, Product, and Engineering to understand business problems, translate them into data requirements, and build reliable data solutions.
The role is based in New York City (HQ) with typically 3+ days per week in the office.
Requirements:
- 2-4+ years of experience in analytics, data science, data engineering, quantitative operations, or similar analytical role
- Highly comfortable with SQL and able to work through complex queries
- Comfortable in Python (pandas, notebooks) for analysis and modeling
- Experience with DBT or capability to quickly learn it
- Strong problem-solving skills; ability to turn ambiguous questions into structured investigations
- Clear communication ability; can explain complex analyses simply to both technical and non-technical teammates
- High ownership mentality; self-directed and able to drive loosely defined problems to solutions
- Highly organized; thinks in systems and processes, brings structure to ambiguity, ruthlessly prioritizes
