You Will:
- Build and enhance machine learning, statistical and causal models for product search, ranking and recommendation that support various business goals
- Work closely with product and engineering managers to develop and deploy solutions with cross-functional support
- Educate our business and engineering partners across the organization on data science concepts (e.g., statistics, machine learning)
- Build models that support real-time events
- Present work to business and engineering leadership
- Identify gaps in existing data, create data product specs, and work with Engineering teams to implement enhanced data tracking
- Partner with Analytics team members and other functions to share insights and best practices, ensuring consistency of data-driven decision-making throughout the org
- Commit to automation and productionalized solutions whenever possible
- Provide mentorship to less experienced data scientists
You Have:
- MS or PhD in statistics, mathematics, analytics, computer science or another quantitative field AND 3+ years of experience as a data scientist in eCommerce or Logistics
- Or, 8+ years of experience as a data scientist in eCommerce or Logistics
- Hands-on experience in building ranking and recommendation models in production environments
- Experience applying descriptive statistics, machine learning, predictive modeling, and visualization techniques to solve challenging business problems
- Statistical and machine learning techniques
- Data Science libraries in a programming or scripting languageModel deployment
- Machine learning workflows
- Python + related ML libraries and frameworks (Ex: PyTorch)
- Proficient in SQL and working with large datasets.
- Experience with cloud platforms (e.g., AWS, GCP, Azure) and model development and deployment technologies (e.g., Databricks).
- Excellent communication and presentation skills (to both technical and business audiences)
Compensation
- Gopuff pays employees based on market pricing and pay may vary depending on your location. The salary range below reflects what we’d reasonably expect to pay candidates. A candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. These ranges may be modified in the future. For additional information on this role’s compensation package, please reach out to the designated recruiter for this role.
- Remote US Range: $160,000 - 260,000
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