Data Science Engineering Manager

Data Science Engineering Manager

Requirements

• 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 (both personally and in leading a team) in Backend Engineering/Machine Learning Engineering shipping and maintaining production-grade systems for internal tools and product users.
• 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, 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: 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, Pulumi; Other platform tools such as FastAPI, Docker, and data visualization tools such as Streamlit or Dash.
• A passion for daily fantasy sports and an understanding of the users, data, and competitive landscape.
• 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.

Responsibilities

• Lead a team to create and maintain sport and user data stream architecture, ensuring data reliability, low latency, and high throughput 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 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.

Preferred Qualifications

• 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.