Data Scientist, Marketing Analytics

Job Description

This role will partner closely with external data vendors and media partners, lead geo-experimentation
design and measurement, and develop propensity and optimization models that guide media investment
decisions.

We value continuous learning, rigorous causal thinking, and the ability to translate complex
analysis into clear, actionable recommendations for marketing leaders.

Responsibilities



* Marketing Mix Modeling: Serve as the internal analytical counterpart to our Marketing Mix
Modeling vendor, reviewing quarterly model refreshes for accuracy, geographic granularity,
variable specification, and channel decay assumptions, and reconciling MMM outputs against
internal experimental results.

Build MMM in-house to measure the return on investment (ROI)
across different advertising channels.

Translate MMM and incrementality outputs into
media/channel budget allocation recommendations, working with internal stakeholders to connect
model output to planning cycles


* Experimentation & Measurement: Design and analyze geo-experiments, media heavy-up/heavy-down designs, and matched-market or synthetic control tests across channels,
including market selection, statistical power/MDE calculations, and exclusion protocols for
confounding risk e.g., seasonal exposure, outlet share, promotions, etc.


* Build Optimization Frameworks: Design randomized holdout structures to measure incremental
value of Email/SMS programs, and develop send-time, frequency, and content optimization
models in partnership with other marketing entities


* Develop Propensity & Customer Models: Build propensity-to-purchase, churn, and channel-response models using transactional, loyalty, and behavioral data to support targeting,
personalization, and customer segmentation


* Analyze Trends & Validate Data: Interpret customer and media performance data for actionable
patterns, assess new data sources for improving model(s) accuracy, and maintain rigorous data
validation standards


* Collaborate for Implementation: Partner with internal teams in advanced analytics work to
operationalize models and ensure measurement infrastructure scales

Qualifications



* Experience: 3-5 years in data science, marketing analytics, or applied statistics, ideally with
direct exposure to marketing measurement in a retail, or omnichannel environment


* Technical Skills: Proficiency in Python and/or R and SQL; hands-on experience with causal
inference methods, Bayesian modeling, experimental design and power analysis, and marketing
mix modeling concepts.

Familiarity with Excel, cloud data warehouses and BI/visualization tools
(Tableau, Looker, GCP, CDP)


* Dynamic Environment: Comfort working in a research-oriented group with multiple concurrent
projects


* Communicate for Impact: Ability to translate statistical and ...