Data Scientist II

Fabletics•El Segundo, CA

2d•$110,000 - $125,000•Hybrid

About The Position

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.

Requirements

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, including a structured approach to diagnosing and resolving blockers independently
Ideal candidate brings foundational AI literacy, including experience using generative AI tools, and a proactive mindset to experiment with new technologies to drive productivity and innovation.

Nice To Haves

Advanced degree in Statistics, Computer Science, Econometrics, or related fields
Experience designing geo experiments or market-level tests for measuring marketing effectiveness at scale
Experience with data visualization tools such as Tableau
Familiarity with marketing tech stack and digital data technologies (Google Analytics, pixel tracking, etc.)
Experience in e-commerce, subscription models, and/or direct-to-consumer brands

Responsibilities

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