Data Science Team Leader at bet365

Denver, Colorado, United States -

Full Time

Start Date

Immediate

Expiry Date

15 Sep, 26

Salary

180000.0

Posted On

17 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, SQL, Machine Learning, Google Cloud Platform, Vertex AI, Scikit-learn, Pandas, NumPy, XGBoost, Google BigQuery, Docker, Kubernetes, CI/CD, Kafka, Stakeholder Management, Team Leadership

Industry

Gambling Facilities and Casinos

Description

Company Description At bet365, we're one of the world's leading online gambling companies, revolutionising the industry since 2000. Founded by Denise Coates CBE, we now employ over 9,000 people and serve over 100 million customers in 27 languages. Our focus on In-Play betting has solidified our market-leading position, offering an unmatched experience across 96 sports and 700,000 streaming events. With over 750 concurrent sporting fixtures at peak and more live sports streamed than anyone else in Europe, we handle over 6 billion HTTP requests daily and process more than 2 million bets per hour at peak. We empower our employees to push boundaries and explore new ideas, cultivating a culture that celebrates and rewards creativity. This offers employees a wealth of opportunities for growth, giving them the opportunity to make a real impact in the world of online gambling. As a forward-thinking company, we’re breaking new ground in software innovation too, redefining what’s possible for our customers worldwide. Job Description This is an exceptional, hands-on, player/coach opportunity to establish, shape, and lead our Data Science capability in the United States. As the Data Science Team Leader, you will be a critical part of our expanding global data organization. You will remain deeply technical and actively involved in writing code, building models, and executing machine learning solutions, while simultaneously mentoring and growing a high-performing team of US-based Data Scientists and Machine Learning Engineers. We are intentionally recruiting for a specific kind of professional: someone with a startup mindset who thrives in fast-paced environments, possesses a strong bias for action, and values execution over theoretical complexity. To succeed, you must be a pragmatic problem solver who enjoys getting their hands dirty while building scalable, production-grade solutions. Excellent stakeholder management is paramount. You will work as a key collaborative partner alongside the US Data Team Lead, Data Product Lead, and AgentOps Team Lead within the wider US Data team, while maintaining strong operational alignment and knowledge sharing with our established UK-based Data Science team. Qualifications Proven experience working in a fast-paced, agile, or startup-like environment. You must have a demonstrated passion for “getting things done” and delivering value iteratively. Prior experience mentoring, coaching, or leading data scientists or engineers while remaining active in code development. A strong track record of designing, building, deploying, and maintaining machine learning models in production environments Superior communication skills with the ability to build strong cross-functional relationships and translate technical concepts into business outcomes for both technical and non-technical audiences. Exceptional programming skills in Python and deep expertise in data science libraries (Scikit-learn, Pandas, NumPy, XGBoost, etc.). Advanced SQL proficiency for querying and manipulating large datasets, preferably within Google BigQuery. Hands-on experience with Google Cloud Platform (GCP), ideally including the Vertex AI ecosystem (Pipelines, Workbench, Endpoints). MSc or PhD in a quantitative discipline (Computer Science, Statistics, Mathematics, Engineering) or equivalent practical industry experience. Familiarity with containerization (Docker, Kubernetes) and CI/CD principles for machine learning. Experience with real-time stream processing or event-driven architectures (e.g., Kafka). Additional Information In this hands-on role you will devise, code, and deploy AI, machine learning and predictive models, leading by example in technical execution and code quality. This is not a pure people-management role. Building, mentoring, and guiding a pragmatic, delivery-focused team of Junior Data Scientists and Machine Learning Engineers, fostering a culture of rapid iteration, continuous learning, and software engineering discipline. Partnering closely with the Data Team Lead, Data Product Lead, and AgentOps Team Lead to align data science initiatives with product roadmaps and platform capabilities. Collaborating regularly with our UK-based Data Science team of technical excellence to share methodology, align on standards, and leverage global technical capabilities. Translating complex, ambiguous business questions into clear data science initiatives, delivering measurable business value through rapid prototyping and deployment cycles. Collaborating with Machine Learning Engineers to champion the adoption of robust MLOps practices on our Google Cloud Platform (GCP) stack, ensuring models are automated, monitored, and scalable. Establish data science workflows, standards, and code repositories from scratch in a new regional office. bet365 provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. Workplace Type: Hybrid Department: AI and Automation (Colorado) Full Time/Part Time: Full Time Shift Pattern: N/A Job Type: Standard Compensation: up to USD 180000 - yearly

Responsibilities

Establish and lead the US Data Science capability by building production-grade machine learning solutions and mentoring a team of data scientists. Collaborate with global stakeholders to align AI initiatives with product roadmaps and implement robust MLOps practices.