Job Description
Fabletics is looking for a Data Scientist to join our Marketing Science & Analytics team.
How do you fit in?
As a Data Scientist at Fabletics, you will play a key role in developing insights that empower our internal teams to scale marketing spend effectively through rigorous media measurement, from ad exposure to conversion. You will collaborate closely with Data Analytics, Media, and a diverse Data Science team with various expertise and focuses.
Tools And Technologies
Expect to leverage Python and SQL daily, with larger projects involving Databricks and Airflow for scale and automation. Flexibility in choosing the best tools for your role is encouraged, reflecting our commitment to ongoing education and exploration of new technologies and modeling methods.
Ideal Candidate
We seek a detail-oriented, solutions-focused Data Scientist with a deep understanding of the media measurement landscape and experience evaluating marketing effectiveness at scale from within a measurement vendor, media agency, or in-house marketing science team. You bring expertise in Bayesian modeling and a strong statistical foundation, approaching ambiguous problems with structure. When faced with an unknown, you don't stop at the question; you identify what you need to find out, articulate a clear path forward, and communicate your reasoning at every step. You take ownership of your projects end-to-end, proactively driving progress and keeping stakeholders informed without being asked. A natural collaborator and clear communicator, you are equally comfortable presenting your methodology to technical peers and explaining your findings to non-technical partners.
What You Will Be Doing
- Incrementality Testing:
- Design and execute rigorous incrementality and lift tests (including geo experiments, holdout tests, and A/B tests) to measure the true causal impact of marketing interventions
- Apply Bayesian modeling and causal inference methods to analyze test results, quantify uncertainty, and produce actionable recommendations for marketing spend optimization
- Own the end-to-end testing process, from proactively structuring experiment designs and clearly defining hypotheses and expected business impact, through execution, analysis, and stakeholder communication of results
- Media Mix Modeling & Attribution:
- Develop and maintain advanced media mix models and attribution systems to assess the impact of digital marketing channels and track touchpoints throughout the customer journey
- Collaborate with cross-functional teams to align models with business goals and ensure comprehensive, accurate data inputs
- Bayesian Modeling:
- Design, develop, and maintain Bayesian statistical models as the primary analytical approach across marketing science projects, with a focus on quantifying uncertainty and informing decision-making
- Apply probabilistic programming tools (PyMC preferred) to build and iterate on models; clearly document methodology, assumptions, and outputs for both technical and non-technical audiences
- Regularly validate and refine models to ensure accuracy; proactively identify opportunities for optimization and propose data-driven solutions to enhance performance
- Data Analysis and Visualization:
- Utilize statistical and machine learning techniques to derive insights from large datasets
- Translate findings into clear, compelling outputs (including dashboards, reports, and stakeholder presentations) and effectively communicate results to both technical and non-technical audiences across marketing, analytics, and technology teams
- Collaboration and Communication:
- Work closely with marketing, analytics, and technology teams to understand data requirements for both marketing and personalization initiatives
- Clearly communicate complex findings and algorithmic recommendations to non-technical stakeholders
What You Can Bring
- 2-4 years of work experience as a Data Scientist
- Fluent in R or Python (Python preferred), SQL, and Git
- Proven experience in media mix modeling, media attribution, and incrementality testing, ideally gained within a measurement vendor, media agency, or sophisticated in-house marketing science function, with familiarity in how measurement solutions are scoped, delivered, and communicated to stakeholders
- Hands-on experience designing and analyzing marketing experiments, including A/B tests, lift tests, and geo experiments; strong command of causal inference methods for interpreting results in real-world, non-ideal conditions
- Strong foundation in statistical modeling, with hands-on experience in Bayesian methods and probabilistic programming (PyMC or equivalent)
- The ability to understand and implement publications in the fields of Data Science/Machine Learning as applied to marketing
- Strong communication skills with the ability to clearly articulate complex topics, project status, methodology, and next steps to both technical and non-technical audiences, includin