Meet Upside:

We created Upside to help communities thrive! Our retail 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 cash back than any other product, and tens 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 local retailers, the consumers they serve, and towards important sustainability initiatives. Our mission, values, and commitment to inclusivity guide our team of more than 300 people worldwide, and the quality of our culture is reflected in the impact we’ve had on communities nationwide. You’ll join a Team whose experience hails from places such as Google, Uber, Amazon, Instacart, Capital One, and MasterCard with proven startup executives and venture capitalists.

Upside was named #2 on Deloitte's 2021 list of Fastest Growing 500 Tech Companies and  #308 in Inc.’s America’s fastest growing private companies of 2022. Upside’s Series D funding was raised in March 2022 and led by General Catalyst at a $1.5 billion valuation. Other investors include Bessemer Ventures and Formation8.

Meet the Data Science team:

The data team is a close-knit group of hardworking, fast-learning individuals that believes in our mission to help communities thrive! We believe in a data-driven approach to decision-making and an encouraging culture that emphasizes learning from our mistakes. We debate and deliberate on the best ideas to improve our processes, and strive for an inclusive atmosphere that nurtures the psychological wellness of all of our teammates.

About the job:

As a Machine Learning Scientist at Upside, you’ll be developing and deploying machine learning models that will personalize the experience of Upside users both inside and outside the app, helping them to find the right product at the right merchant at the right price at the right time. You will work closely with our product and marketing teams to identify areas where machine learning algorithms can improve the user experience, and oversee the development and deployment of those models from conception to evaluation.

What you'll do:

• Develop and iterate on algorithms in the personalization and recommendation space that will return real-time predictions and recommendations in-app
• Own all aspects of model development from initial research and development to deployment
• Analyze performance of models in champion-challenger tests and communicate results to relevant stakeholders
• Mentor other machine learning scientists and data scientists on the team

What you need:

• A PhD in a quantitative field 
• Experience with Python and SQL
• At least 3 years of experience using recommender systems, ranking algorithms, and similar techniques to personalize user experience on an app or website
• A proven track record of developing new machine learning models or improving existing models that led to measurable improvements in core business metrics
• Solid understanding of statistics, machine learning, and deep learning
• Strong communication skills
• Experience with deploying models into production preferred

The fine print:

• Notice to recruiters and placement agencies: This is an in-house search with a dedicated resource. Please do not submit resumes to any person or email address at Upside. Upside is not liable for, and will not pay, placement fees for candidates submitted by any party or agency other than its approved recruitment partners.

At Upside, we believe that diversity drives innovation. Our differences are what make us stronger. We‘re passionate about building a workplace that represents a variety of backgrounds, skills, and perspectives and does not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. Everyone is welcome here. Come join us!

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