PrizePicks · 3 hours ago

Data Science Engineer

United States

Full-time

Remote

Mid Level

3+ years exp

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Fantasy SportsGaming

Growth Opportunities

No H1B

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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 the operationalization of DS/ML assets, ensuring model output quality, stability, and scalability.

Lead the design and implementation of the data and MLOps stack required for real-time pricing models and contribute to architecture evaluations and decisions for our growing data product roadmap.

Work cross-functionally with Engineering, QA, and Product teams to enable the creation and distribution of highly visible, real-time, in-game micro market offerings to the PrizePicks platform.

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

Data ScienceMLOpsCloud-based data streamingSQLPythonGoRustGCPGitPyTestScikit-learnPyTorchTensorflowPrefectAirflowTerraformPostgresBigQueryBigTableCloud StorageCloud Compute EngineCloud FunctionsKubernetes EnginePyUnitNose2Deep learningGradient boostingBayesian methodsGenerative modelsCloud Composer

Required

3+ years of experience in a data science, machine learning engineer, or data-oriented software engineering role creating and pushing end-to-end data science pipelines and MLOps assets to production.

Experience building and optimizing cloud-based data streaming pipelines and infrastructure.

Experience exposing real-time predictive model outputs to production-grade systems leveraging large-scale distributed data processing and model training.

Experience with 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, Nose2, etc., Common ML and DL frameworks: scikit-learn, PyTorch, Tensorflow, Modeling methods: classical ML techniques, deep learning, gradient boosting, bayesian methods, generative models, Data pipeline and workflow tools: Prefect, Airflow, Cloud Composer, Serverless Framework, Monitoring and Observability platforms: Datadog, ELK stack, Infrastructure as Code platforms: Terraform, Google Cloud Deployment Manager, Ansible, Other platform tools such as Redis, FastAPI, Streamlit.

Strong 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.