At Underdog, we make sports more fun.
Our thesis is simple: build the best products and we’ll build the biggest company in the space, because there’s so much more to be built for sports fans. We’re just over five years in, and we’re one of the fastest-growing sports companies ever, most recently valued at $1.3B. And it’s still the early days.
We’ve built and scaled multiple games and products across fantasy sports, sports betting, and prediction markets, all united in one seamless, simple, easy to use, intuitive and fun app.
Underdog isn’t for everyone. One of our core values is give a sh*t. The people who win here are the ones who care, push, and perform. If that’s you, come join us.
Winning as an Underdog is more fun.
- Collaborate with other data scientists to build and iterate on models for personalized recommendations, targeting, and user segmentation.
- Lead personalization initiatives that span modeling, experimentation, and implementation to improve user experience and retention.
- Build and deploy machine learning models such as recommendation systems, targeting algorithms, segmentation, and ranking models.
- Design and analyze A/B tests and other experiments to evaluate the effectiveness of personalization strategies.
- Collaborate closely with Product, Engineering, Marketing, and Data Engineering to bring personalization models into production.
- Develop clean, maintainable code and contribute to reusable pipelines, feature stores, and evaluation frameworks.
- Translate data insights into compelling stories and actionable strategies for technical and non-technical audiences.
- A degree in Math, Physics, Statistics, Economics, Computer Science, or a similar domain. MS degree preferred.
- 7+ years of experience in data science, machine learning, or a related technical role.
- Hands-on experience with recommendation engines, targeting systems, ranking models, or personalization algorithms.
- Strong proficiency in Python for modeling and data manipulation.Advanced SQL skills and experience querying large, complex datasets.
- Solid foundation in statistics, hypothesis testing, and experimental design.
- Familiarity with cloud-based tools and platforms (e.g., AWS, GCP, Snowflake, dbt, Airflow).
- Proven ability to partner cross-functionally and influence product decisions with data.
- Experience with uplift modeling, multi-armed bandits, or causal inference.
- Prior work in industries such as fantasy sports, sports betting, mobile gaming, or other B2C tech companies.
- Exposure to real-time personalization pipelines or recommender systems at scale.
- Familiarity with tools like MLflow, SageMaker, or Feature Stores.
Our target starting base salary range for this position is between $180,000 and $210,000, plus target equity. The starting base salary will depend on a number of factors including the candidate’s skills and experience, among other things.
- Unlimited PTO for full-time employees (we're extremely flexible with the exception of the first few weeks before & into the NFL season)
- 16 weeks of fully paid parental leave
- A $500 home office allowance
- A connected virtual-first culture with a highly engaged distributed workforce
- 5% 401k match, FSA, company paid health, dental, vision plan options for employees and dependents
#LI-REMOTE
Underdog is an equal opportunity employer and doesn't discriminate on the basis of creed, race, sexual orientation, gender, age, disability status, or any other defining characteristic.
California Applicants: Review our CPRA Privacy Notice here.
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