Data Science Team Lead/Manager

hackajob is collaborating with Bet365 to connect them with exceptional professionals for this role.

This is an exceptional, hands-on, player/coach opportunity to establish, shape, and lead our Data
\n Science capability in the United States. As the Data Science Team Leader, you will be a critical part
\n of our expanding global data organization.
\n You will remain deeply technical and actively involved in writing code, building models, and
\n executing machine learning solutions, while simultaneously mentoring and growing a high
\n performing team of US-based Data Scientists and Machine Learning Engineers.
\n We are intentionally recruiting for a specific kind of professional: someone with a startup mindset
\n who thrives in fast-paced environments, possesses a strong bias for action, and values execution
\n over theoretical complexity. To succeed, you must be a pragmatic problem solver who enjoys
\n getting their hands dirty while building scalable, production-grade solutions.
\n Excellent stakeholder management is paramount. You will work as a key collaborative partner
\n alongside the US Data Team Lead, Data Product Lead, and AgentOps Team Lead within the wider
\n US Data team, while maintaining strong operational alignment and knowledge sharing with our
\n established UK-based Data Science team.

Main Responsibilities:
\n • In this hands-on role you will devise, code, and deploy AI, machine learning and predictive
\n models, leading by example in technical execution and code quality. This is not a pure
\n people-management role.
\n • Building, mentoring, and guiding a pragmatic, delivery-focused team of Junior Data
\n Scientists and Machine Learning Engineers, fostering a culture of rapid iteration,
\n continuous learning, and software engineering discipline.
\n • Partnering closely with the Data Team Lead, Data Product Lead, and AgentOps Team Lead
\n to align data science initiatives with product roadmaps and platform capabilities.
\n • Collaborating regularly with our UK-based Data Science team of technical excellence to
\n share methodology, align on standards, and leverage global technical capabilities.
\n • Translating complex, ambiguous business questions into clear data science initiatives,
\n delivering measurable business value through rapid prototyping and deployment cycles.
\n • Collaborating with Machine Learning Engineers to champion the adoption of
\n robust MLOps practices on our Google Cloud Platform (GCP) stack, ensuring models are
\n automated, monitored, and scalable.
\n • Establishing data science workflows, standards, and code repositories from scratch in a
\n new regional office.
\n The skills and experience to help you perform in the role:
\n • Proven experience working in a fast-paced, agile, or startup-like environment. You must
\n have a demonstrated passion for “getting things done” and delivering value iteratively.
\n • Prior experience mentoring, coaching, or leading data scientists or engineers while
\n remaining active in code development.
\n • A strong track record of designing, building, deploying, and maintaining machine learning
\n models in production environments
\n • Superior communication skills with the ability to build strong cross-functional relationships
\n and translate technical concepts into business outcomes for both technical and non
\n technical audiences.
\n • Exceptional programming skills in Python and deep expertise in data science libraries
\n (Scikit-learn, Pandas, NumPy, XGBoost, etc.).
\n • Advanced SQL proficiency for querying and manipulating large datasets, preferably within
\n Google BigQuery.
\n • Hands-on experience with Google Cloud Platform (GCP), ideally including the Vertex AI
\n ecosystem (Pipelines, Workbench, Endpoints).
\n • MSc or PhD in a quantitative discipline (Computer Science, Statistics, Mathematics,
\n Engineering) or equivalent practical industry experience.
\n • Familiarity with containerization (Docker, Kubernetes) and CI/CD principles for machine
\n learning.
\n • Experience with real-time stream processing or event-driven architectures (e.g., Kafka).