Staff Data Science Engineer

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Requirements:

• 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.

Responsibilities:

• 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.