Senior Applied Data Scientist / ML Engineer
New
W
WorkWaveOperational intelligence
Remote (UK-based)Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years in applied data science, ML engineering, or data engineering that included owning models in production
- Required Skills
- AWSPythonSQLSnowflakedbtscikit-learn
Requirements
- Have 5+ years of experience in applied data science, ML engineering, or data engineering that included owning models in production.
- Have built and shipped at least one model into a real product.
- Have strong Python skills and experience with applied ML libraries for tabular problems.
- Have solid SQL expertise.
- Have depth in supervised learning, forecasting, ranking, recommendation, or optimisation.
- Have experience modelling messy operational data.
- Be able to build data models and feature pipelines with dbt and Snowflake.
- Have experience with deployment, monitoring, and drift detection; the company uses AWS.
- Have quantified the business impact of a shipped model, such as adoption, outcomes, or ROI.
- Be able to explain technical trade-offs to product, engineering, and non-technical business stakeholders.
- B2B SaaS experience is a strong plus.
- Experience with coding agents, decision intelligence, forecasting, customer behaviour modelling, workforce or route optimisation, operational intelligence products, or LLM or agentic workflows shipped into a product is a bonus.
- Prior experience as a senior or lead scientist or engineer responsible for guiding technical direction is a bonus.
Responsibilities
- Frame ambiguous customer problems and determine whether machine learning is an appropriate solution.
- Learn the meaning of customer operational data and domain concepts.
- Create needed features and data models in Snowflake and dbt.
- Determine how predictions reach customers so they can understand, trust, and act on them.
- Report realized model impact to Product and the business.
- Define offline and online evaluation for model quality, drift, and reliability.
- Design A/B tests or causal analyses to assess customer outcomes.
- Deploy, test, version, monitor, and detect drift in models.
- Partner with Product Managers and Software Engineers to integrate ML into product workflows.
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