Senior Data Science Engineer

View full description

Requirements:

• 5+ years of experience in Backend Engineering/Machine Learning Engineering, shipping and maintaining production-grade systems for internal tools and product users.
• 2+ 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.
• Graduate degree in Computer Science, Statistics, Mathematics, Informatics, Information Systems or other quantitative field

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.
• Build and own rigorous monitoring, alerting, and documentation processes, and work with Engineering teams to ensure complete feature uptime.
• Grow 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.