The Data Science Team
You will be part of the data science team. We work cross-functionally with other teams to bring data-driven impact to Fabulous and its business units like Product Growth, User Acquisition and Finance.
Practically the team handles 3 data areas:
- Data Project Management: Based on business needs, we sort requests, refine requirements and ACs with business and/or data stakeholders, prioritise, plan and executive while communicating proactive and frequently to ensure visibility and a well connected feedback loop with involved parties (reviewer, data stakeholder, business stakeholder)
- Applied Analytics & Data Science: Data Exploration, Defining appropriate and agile analytics approach. We aim for simplicity and interpretability but don't shy away from complexity when faced with it. All new projects have a strong Data Science component during the first MVP iteration
- Analytics Engineering: Data Modelling and Transformations to serve build, maintain and scale our Analytics Pipelines. Practically, as soon as an MVP gets validated by different stakeholders, we start implementing it and improving it iteratively in our dbt project. Testing and data observability is a highly important component. Proper architecture that helps manage TechDebt is another key element here as well.
All members of the team are expected to excel in all three areas to be autonomously impactful.
We work in an agile manner by splitting bigger projects into iterations that rarely expand beyond 3 weeks to ensure impact.
We have a modern cloud-based Data-Stack (Fivetran - Big Query - dbt - Amplitude - Metabase - Looker Studio) and want to consolidate our ranks with a capable well-rounded Senior Data Scientist who can integrate our agile context smoothly and bring value quickly.
Expectations | Duties
This role is highly critical to the continuous success of the data science team:
- You will work on diverse high priority business projects to make Fabulous more data driven. Those projects will be in close collaboration with business teams and will aim for clear and tangible business impact like (improving accuracy of metrics, analytically exploring new growth perspectives, building well-tested analytics reporting pipelines, investigating and correcting data discrepancies, applying statistics and ML effectively...)
- You will be responsible for contributing effectively to our code base: building, testing, reviewing and maintaining solid analytics pipelines using SQL and dbt. Help managing TechDebt and improving engineering practices and the project's architecture are also important responsibilities for this role.
- You are expected to gradually own some aspects of the team's responsibilities (some parts of the code base, become the main point of contact with at least one business team, have a strong saying in how the analytics project's arch