Senior Data Scientist

As a Senior Data Scientist, you will accelerate our end-to-end machine learning lifecycle, building on our strong data science foundation to scale impact and automate business decisions. The data science team is at the forefront of driving business decisions, we are now scaling our impact with a focus on automation and advanced MLOps practices on Google Cloud.

What You Will Do

You will be responsible for the end-to-end lifecycle of machine learning solutions that optimize our Sports and Gaming products, from development to automated deployment and monitoring. You will be responsible for building and implementing frameworks for automated model testing, validation, and monitoring using tools such as Vertex AI Model Monitoring to detect drift and ensure performance at scale.

Why It Might Be a Fit

This is an exciting opportunity to apply cutting-edge data science and MLOps principles in a fast-paced, high-impact environment, tackling complex challenges in areas like Trading, Fraud, Responsible Gaming, and Personalization.

Requirements

• PhD or MSc in a quantitative field such as Computer Science, Statistics, or Engineering, or equivalent industry experience delivering complex data science projects.
• Demonstrable experience deploying and maintaining machine learning systems in a production environment with measurable business impact.
• Strong programming skills in Python and deep expertise in data science libraries such as, Scikit-learn, Pandas, NumPy, XGBoost.
• Advanced proficiency in SQL, with hands-on experience querying and manipulating large, complex datasets, preferably with Google BigQuery.
• Extensive hands-on experience with Google Cloud Platform (GCP), including building and automating ML workflows with Vertex AI pipelines, managing datasets, training models, and deploying to Vertex AI.
• Experience using collaborative development environments such as Vertex AI Workbench for rapid prototyping, exploration, and analysis.
• Experience leveraging other core GCP services such as BigQuery, Cloud Storage, and Cloud Functions to build end-to-end data solutions.
• Solid understanding of CI/CD principles and tools such as Cloud Build or GitLab CI for automating ML workflows.
• Experience with containerization such as Docker, Kubernetes/GKE.

Benefits

• Equal employment opportunities
• Automated model testing, validation, and monitoring using tools such as Vertex AI Model Monitoring
• A/B tests and other experiments to measure the impact of models and strategies
• Research and development of innovative data science and MLOps techniques, tools, and methodologies
• Mentorship and leadership opportunities for other data scientists