Data Scientist

Meet Upside:

We created Upside to transform brick-and-mortar commerce. Our technology uses the sophistication of online retail-profit measurement, attribution, and incrementality-to provide users with more value on their everyday purchases and brick-and-mortar businesses with new, profitable customers. We've helped millions of users earn 2 to 3 times more cashback than any other product, and hundreds of thousands of brick-and-mortar businesses earn measurable profit. Billions of dollars in commerce run through the Upside platform every year, and that value goes directly back to our retailer partners, the consumers they serve, and important sustainability initiatives.

The Impact You'll Make:

You'll work with peers who have experience developing ML models designed for production with the support of a dedicated engineering team. You'll align closely with analysts, stakeholders, and data engineers across the company to define solutions to pressing problems. Your work will directly and measurably impact the company's revenue by innovating on our offer pricing and recommendation systems. We foster a collaborative environment where you'll have ownership of your modeling efforts and actively participate in project creation and planning.

Responsibilities

• Own the entire machine learning lifecycle - from framing ambiguous business problems to delivering measurable impact in production
• Dig into complex data to design, run, and refine analyses through well-structured, performant SQL
• Engineer, validate, and deploy real-time ML models-including building the data pipelines, writing production inference code, and monitoring model performance
• Design and execute experiments to measure business impact, troubleshoot unexpected outcomes, and iterate quickly
• Effectively translate results of experiments and analyses into actionable insights for stakeholders and leadership with a variety of technical backgrounds

• Master's degree or Ph.D. in Statistics, Data Science, Economics, Physics, or a related technical field or 4+ years of experience as a data scientist or advanced analytics role
• Proven track record framing open-ended problems, conducting analyses, and producing data-driven recommendations
• SQL mastery, experience working with large data-sets on the cloud a plus
• Proficiency in Python and statistical and machine learning libraries (e.g., scikit-learn, statsmodels, xgboost)
• Comfort with ambiguity and constraints - you thrive in situations with incomplete specs, evolving requirements, and operational challenges, and can deliver high-quality solutions despite them

• End-to-end ML Ops experience including building live feature-stores, writing production-ready inference code or a strong desire to learn these skills
• Experience with data warehouse and cloud environments (i.e. Snowflake and AWS)
• Experience with building recommendation, pricing models, and causal inference

• Programming language: Python
• Data tools: dbt, SQL, Snowflake
• Cloud platform: AWS
• Version control: Git
• Machine learning libraries: scikit-learn, statsmodels, etc.
• Data exploration and visualization: Jupyter, Hex, GSheets
• ML Ops platform: JFrog ML

• Medical, dental, and vision coverage starting on Day 1
• Equity (ISOs)
• 401(k) program
• Family planning programs + paid parental leave
• Physical fitness and wellness memberships
• Emotional and mental health support programs
• Unlimited PTO + 10 paid federal holidays + our annual, week-long Winter Break
• Flexible work environment
• Lunch reimbursement for in-office employees
• Employee Resource Groups
• Learning and Development stipend
• Transparent culture
• Amazing mission!