Hands-On Data Science Team Lead — Fast-Paced Impact

Bet365 Company; Denver (CO); On-site; USD 155,000 - 165,000; Full time

Job summary

bet365 seeks a Data Science Team Leader to establish, shape, and lead its US Data Science capability. You will stay hands-on—writing code, building models, and deploying production ML solutions—while mentoring a team of US-based Data Scientists and ML Engineers. You’ll coordinate with UK peers and drive rapid, scalable data initiatives.

You will lead by example in technical execution, foster rapid iteration, and ensure robust MLOps on GCP as part of global data capabilities.

Qualifications

• Proven experience leading data science teams in fast-paced environments.
• Hands-on with building, deploying, and maintaining ML models in production.
• Strong Python programming and data-science library expertise (scikit-learn, pandas, numpy, xgboost).
• Advanced SQL skills; experience with BigQuery preferred.
• Excellent communication and stakeholder management.

Responsibilities

• Lead and mentor a US-based data science and ML engineering team.
• Write code, build models, and deliver production-grade ML solutions.
• Collaborate with US Data Team Lead, Data Product Lead, and AgentOps Team Lead.
• Partner with UK team to align standards and share methodology.
• Establish data science workflows and MLOps practices on GCP.

Skills

Python

Leadership

Production ML

SQL

BigQuery

GCP

Vertex AI

Docker

Kubernetes

Kafka

MLOps

Education

MSc/PhD in quantitative discipline

Tools

Google Cloud Platform (GCP)

Vertex AI

Docker

Kubernetes

Kafka

BigQuery

Job description

bet365 seeks a Data Science Team Leader to establish, shape, and lead its US Data Science capability. You will stay hands-on—writing code, building models, and deploying production ML solutions—while mentoring a team of US-based Data Scientists and ML Engineers. You’ll coordinate with UK peers and drive rapid, scalable data initiatives.

You will lead by example in technical execution, foster rapid iteration, and ensure robust MLOps on GCP as part of global data capabilities.