Job Description

The PrizePicks Analytics Team is responsible for building and maintaining analytical tools and products to support the PrizePicks business across all departments — at the core of these operations is data. As Engineering Manager — Data Science Engineering, you will lead a team to develop, test, maintain, and evolve initiatives in streaming data, productionized algorithms, and MLOps infrastructure to mature PrizePicks’ digital offering.

What you’ll do:

• Lead a team to create and maintain sport and user data stream architecture, ensuring data reliability in both speed and quality for both raw and transformed data pipelines. 
• Collaborate with our Data Science team to determine the best paths for operationalizing 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 and alerting services, and work with Engineering and DevOps teams to ensure stability and complete uptime of our production services.
• Solidify and disseminate information and ideas through rigorous documentation, roadmaps, and knowledge transfer processes both within and across teams.
• Act as a thought leader in the broader PrizePicks technology org, staying abreast of and implementing novel technologies, and stewarding best practices both upward and downward within your direct team and to other people leaders/collaborators alike.

What you have:

• Track Record
  • 3+ years in a people leadership role, managing and growing a team of Associate through Staff level Data Science Engineers/Machine Learning Engineers/ML-focused Software Engineers.
  • Extensive experience working cross-functionally with data engineering, data science, product, and engineering teams, as well as external data providers and 3rd party services.
  • Proven experience (personally and leading a team) in shipping and maintaining production-grade systems for internal tools and product users.
• Role Specific
  • Experience with simulation frameworks, personalization, and/or near real-time consumer-facing machine learning implementations.
  • Strong understanding of software development life cycle principles related to shipping critical and always-on services in the cloud.
  • Experience/familiarity in most of the following technology/stack areas:
    • Scripting languages: Python, SQL.
    • SQL/NoSQL databases/warehouses: Postgres, BigQuery, BigTable.
    • Cloud platform services in GCP and analogous systems: Cloud Storage, Cloud Compute Engine,Cloud Functions, Kubernetes Engine, Redis.
    • Code testing libraries: PyTest, PyUnit, Nose2, etc.
    • Common ML and DL frameworks: scikit-learn, PyTorch, Tensorflow.
    • Modeling methods: classical ML techniques, exposure to deep learning, gradient boosting, bayesian methods, and generative models.
    • MLOps tools: DataBricks, MLFlow, Kubeflow, DVC.
    • Data pipeline and workflow tools: Prefect, Airflow, Cloud Workflows, Cloud Composer, Serverless Framework.
    • Monitoring and Observability platforms: Datadog, ELK stack, Grafana.
    • Infrastructure as Code platforms: Terraform, Google Cloud Deployment Manager, Pulumi.
    • Other platform tools such as FastAPI and data visualization tools such as Streamlit or Dash.
• Industry Specific
  
  • A passion for daily fantasy sports and an understanding of the users, data, and competitive landscape.
• Personal Attributes
  • Purposeful people growth, mentorship experience, and coaching perspective.
  • 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. Advanced degree preferred.

What makes you stand out:

• Experience building with or leading a team using Rust, Go, or other high-performance programming languages.
• Experience building real-time production data science pipelines in a daily fantasy sports or odds-making business.
• Experience shipping products in both the D2C and B2B SaaS operational spaces.
• Experience deploying and upholding regulated data products.

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