Job description
At PrizePicks, we are the fastest-growing sports company in North America, as recognized by Inc. 5000. As the leading platform for Daily Fantasy Sports, we cover a diverse range of sports leagues, including the NFL, NBA, and Esports titles like League of Legends and Counter-Strike. Our team of over 450 employees thrives in an inclusive culture that values individuals from diverse backgrounds, regardless of their level of sports fandom. Ready to reimagine the DFS industry together?
The Analytics Team is responsible for building and maintaining analytics tools and workflows to support the PrizePicks business across all departments - at the core of these operations is data. As a Staff Data Science Engineer, you will be developing, maintaining, testing, and leading projects regarding streaming data and MLOps infrastructures to enable PrizePicks to offer real-time simulation based market pricing within the product.
What you’ll do:
- Create and maintain optimal sport data stream architecture, ensuring data reliability in both speed and quality for both raw and transformed data pipelines.
- Partner with Data Science to determine best paths for operationalization of DS/ML assets, ensuring model output quality, stability, and scalability.
- Steer the design, implementation, and deployment of the data, MLOps, and API stack required for real-time pricing models, personalization/recommendations, risk management tooling, and other critical functions by contributing to architecture evaluations and decisions for the evolving data product roadmap.
- Partner cross-functionally with Engineering, QA, and Product teams to enable the creation and distribution of highly visible and real-time data products to the PrizePicks platform.
- Empower teams to build and own rigorous monitoring, alerting, and documentation processes, and work with Engineering teams to ensure complete feature uptime.
- Act as a thought leader in the broader PrizePicks technology org, staying abreast of and implementing novel technologies, and disseminating knowledge and best practices to junior members of the team and collaborators alike.
What you have:
- 7+ years of experience in Backend Engineering/Machine Learning Engineering shipping and maintaining production-grade systems for internal tools and product users.
- 3+ years of experience acting as technical lead and providing mentorship and feedback to junior engineers and scientists.
- Extensive experience exposing real-time predictive model outputs to production-grade systems leveraging large-scale cloud-based data streaming pipelines and infrastructure.
- Extensive experience working cross-functionally with data engineering, data science, product, and engineering teams, as well as external data providers and 3rd party services.
- Experience in most of the following:
- SQL/NoSQL databases/warehouses: Postgres, BigQuery, BigTable,
- Scripting languages: SQL, Python, Go, Rust.
- Cloud platform services in GCP and analogous systems: Cloud Storage, Cloud Compute Engine, Cloud Functions, Kubernetes Engine.
- Code version control: Git
- Code testing libraries: PyTest, PyUnit, etc
- Common ML and DL frameworks: scikit-learn, PyTorch, Tensorflow
- Modeling methods: classical ML techniques, deep learning, gradient boosting, bayesian methods, generative models.
- MLOps tools: DataBricks, MLFlow, Kubeflow, DVC.
- Data pipeline and workflow tools: Airflow, Argo Workflows, Cloud Workflows, Cloud Composer, Serverless Framework.
- Monitoring and Observability platforms: Prometheus, Grafana, Datadog, ELK stack.
- Infrastructure as Code platforms: Terraform, Google Cloud Deployment Manager.
- Other platform tools such as Redis, FastAPI, Docker and data visualization tools such as Streamlit or Dash.
- Excellent organizational, communication, presentation, and collaboration experience with organizational technical and non-technical teams
- Graduate degree in Computer Science, Statistics, Mathematics, Informatics, Information Systems or other quantitative field
What makes you stand out:
- Experience building real-time production data science pipelines in a daily fantasy sports or oddsmaking business
Where you’ll live:
- While we prefer candidates based in Atlanta, we are open to qualified applicants from anywhere in the U.S. and are willing to consider remote candidates. #LI-Remote
Working at PrizePicks:
The typical salary range for this position is $200,000 to $270,000. At PrizePicks, we consider your role, level, and where you’ll be working when determining our salary ranges. The compensation info you see on our job postings gives you an idea of the starting pay range for the position. Your actual pay within that range will depend on your specific work location, as well as your skills, experience, and education. Your recruiter will be happy to chat more about the specific pay range for your location and how we arrived at it during the hiring process.
This application period will remain open for 30 days. We’re committed to finding the best candidate, so this date may be adjusted, and any changes will be reflected in this posting.
Date Posted: May 6th, 2025
Benefits you’ll receive:
In addition to your great compensation package, full-time employees will be eligible for the following perks:
- Company-subsidized medical, dental, & vision plans
- 401(k) plan with company match
- Annual bonus
- Flexible PTO to encourage a healthy work/life balance (2 weeks STRONGLY encouraged!)
- Generous paid leave programs, including 16-week paid parental leave and disability benefits
- Workplace flexibility and modern work schedules focused on getting the job done, not hours clocked
- Company-wide in-person events and team outings
- Lifestyle enhancement program
- Company equipment provided (Windows & Mac options)
- Annual performance reviews with opportunities for growth and career development
You must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.
PrizePicks is an Equal Opportunity Employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.
