Senior Applied Data Scientist / ML Engineer

Role overview Turn operational data into product features that help customers make better decisions and run their businesses more effectively. This is an applied role spanning problem framing, data preparation, modeling, deployment, and measurement; success means customers can understand and act on model outputs, with impact demonstrated in real use.

Responsibilities - Frame customer problems and determine whether machine learning is the right way to address them. - Learn the operational context behind the data, including how concepts such as routes, crews, and service history are represented. - Build the data and features needed for modeling, including transformations in Snowflake and dbt. - Put predictions into product workflows in a form customers can understand, trust, and use. - Define offline and online evaluations for quality, reliability, and drift, and use experiments or causal analysis to assess customer outcomes. - Own delivery and operation, including testing, deployment, versioning, monitoring, and communicating measured impact.

Requirements - 5+ years in applied data science, machine learning engineering, or data engineering, including ownership of production models and at least one model shipped in a real product. - Strong Python and SQL skills, with applied experience using tools such as scikit-learn, XGBoost, LightGBM, statsmodels, or Prophet. - Modeling depth in areas such as supervised learning, forecasting, ranking, recommendation, or optimization. - Experience working with messy operational data rather than only prepared benchmark datasets. - Ability to build data models and feature pipelines, deploy models, and monitor performance and drift; the environment uses dbt, Snowflake, and AWS. - Skill in measuring model outcomes and explaining technical trade-offs to product, engineering, and business partners.

Nice to have B2B SaaS experience, coding-agent experience, or work on decision intelligence, forecasting, customer behavior, workforce or route optimization, or operational intelligence. Prior technical leadership or productized LLM and agent workflows is also useful.

Benefits and work setup Remote within the UK, with flexible working options. Applicants must already have the legal right to work in the UK; visa sponsorship is not available. Benefits include an employer-matched pension, flexible time off plus UK bank holidays, enhanced parental leave, learning resources, and a 24/7 employee assistance program.