Data Science Team Lead/Manager

Main Responsibilities

• Devise, code, and deploy AI, machine learning and predictive models, leading by example in technical execution and code quality.
• Build, mentor, and guide 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.
• Partner closely with the Data Team Lead, Data Product Lead, and AgentOps Team Lead to align data science initiatives with product roadmaps and platform capabilities.
• Collaborate regularly with our UK-based Data Science team of technical excellence to share methodology, align on standards, and leverage global technical capabilities.
• Translate complex, ambiguous business questions into clear data science initiatives, delivering measurable business value through rapid prototyping and deployment cycles.
• Collaborate 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.

The Skills and Experience Needed

• Proven experience working in a fast-paced, agile, or startup-like environment.
• 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).