Zipcar is looking to solve core business areas such as fleet planning, pricing strategy, member segmentation, and demand forecasting by building and scaling models
Requirements
- Proven experience with predictive modeling, time series forecasting, pricing algorithms, or classification models
- Strong Python skills for statistical modeling and knowledge of modern ML platforms (e.g. PyTorch/TensorFlow, scikit-learn)
- Advanced SQL and experience working with large-scale cloud data warehouses (Redshift, Snowflake, etc.)
- Experience deploying models in production environments (using tools like Airflow, dbt, or AWS)
- Knowledge of machine learning model evaluation, and performance tuning best practices
- Familiarity with software development workflows (version control, code review, reproducibility)
- Experience with geospatial modeling, especially using Uber’s H3 library
Responsibilities
- Improve fleet demand forecasting models
- Build new predictive models for pricing sensitivity, member segmentation, and campaign targeting
- Monitor model performance, retrain as needed, and manage model lifecycle in production
- Partner with Engineering to move models from prototype to production and partner with the Product team to prioritize future enhancements
- Build trust in modeling outputs by documenting assumptions, limitations, and performance
- Evangelize data science best practices across the company and help level up analytical maturity
- Translate complex modeling outcomes into clear, actionable recommendations
Other
- Bachelor's or Master’s degree in Statistics, Computer Science, Applied Math, or a related field and a minimum of 3 years of professional experience
- Strong written and verbal communication skills—able to clearly explain models to non-technical audiences
- Comfort working with ambiguity, unstructured problems, and evolving data sources
- Curiosity and interest in leveraging AI and LLM tools to improve workflows
- Experience mentoring other Analysts or acting as a tech lead
