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Graduate data engineering role focused on building and maintaining SQL/dbt transformation pipelines, Python ingestion services, API data pipelines, and data quality processes. The role includes production coding, testing, debugging, code reviews, cloud infrastructure exposure, AI-assisted engineering, and collaboration with engineers, analysts, and product stakeholders. It is designed for recent graduates with foundational SQL and Python experience and offers mentorship, training, and cross-functional development opportunities.

Build and maintain full-stack features using React or Next.js, Python, databases, and APIs. Write tested code, participate in reviews and design discussions, resolve bugs, improve test coverage, and collaborate with product, design, and engineering teams throughout the software development lifecycle. The role also involves AI-assisted development, production monitoring, and progressively increasing ownership of system areas.

Supports incrementality testing and marketing mix modeling by validating client data, assessing experimental designs, running analyses, interpreting diagnostics, investigating model-trust queries with SQL, and reconciling measurement results. The role also includes client explainer sessions, documentation, methodology fact-checking, incident logging, and workflow improvement. Work is performed under detailed supervision with structured development toward independent testing, MMM delivery, and automated trust monitoring.

Build, ship, and maintain production data science models for attribution, marketing mix modelling, incrementality testing, and brand impact measurement. Independently select modelling approaches, debug code, collaborate with QA, communicate with clients as a modelling expert, use AI tooling and automation, and mentor junior colleagues.

Develop and maintain data science models for marketing measurement products, including attribution, forecasting, optimization, and experimentation. Own analytical investigations, model backtesting, production bug fixes, and pipeline work. Write and review Python and SQL production code, debug unfamiliar repositories, use AWS and QA tooling, and communicate findings to clients and colleagues. The role also involves applying machine learning methods, reviewing AI-assisted code, clarifying requirements, and progressively taking ownership of technical areas.

The Marketing Science Lead will support customer success by operationalizing advanced modeling, conducting incrementality tests, and improving data literacy across teams.

Join a Customer Success team to drive customer outcomes, onboarding, and retention by turning data into actionable insights. Build trust in models and data quality, solve customer problems, collaborate with Account Managers and Product, and use Gen AI tools to increase efficiency.

Graduate Programme Customer Success coordinators own client portfolios, manage onboarding, drive product adoption and retention, prepare insight decks, run training, identify and mitigate renewal risks, build strong client relationships, and collaborate cross-functionally while leveraging data (Excel) and generative AI tools to increase efficiency.