Responsibilities
- Design and implement proof-of-concept systems with defined success metrics to enable clear go/no-go decisions based on measurable outcomes.
- Construct ETL/ELT workflows to produce new datasets for analytics and machine learning, including pipelines for feature engineering used in modeling and reporting.
- Apply supervised and unsupervised machine learning methods to solve business problems such as predicting user behavior, customer grouping, churn forecasting, content suggestions, and anomaly identification.
- Create simple Python tools and interactive notebook applications that enable non-technical teams to explore model results or access processed data.
- Build prototypes of AI-powered internal tools using large language model APIs, semantic search, and retrieval-augmented generation to unlock value from existing content repositories.
- Partner with analytics teams to standardize data definitions, shared datasets, and performance metrics across experiments and production systems.
- Coordinate with engineering staff to outline technical requirements for scaling successful prototypes, providing clean, documented code and architectural documentation.
- Promote improved data quality and tracking practices by collaborating with Product and Engineering teams on instrumentation and data governance standards.
Work Arrangement
Hybrid — London
