Data Scientist - Marketing Measurement & Causal InferencePosition SummaryWe are seeking a highly analytical
Data Scientist with expertise in marketing measurement, causal inference, experimentation, and advanced statistical modeling. This role will focus on developing measurement frameworks that help quantify marketing effectiveness, optimize investment decisions, and drive data-informed business strategy.
The ideal candidate combines strong scientific rigor with practical business application, leveraging experimentation, machine learning, and advanced analytics to uncover actionable insights from complex datasets. This position offers the opportunity to work on large-scale measurement challenges while partnering closely with marketing, analytics, and engineering teams to deliver impactful solutions.
Key Responsibilities- Develop and enhance Marketing Mix Models (MMM) to measure channel effectiveness, optimize budget allocation, and quantify marketing ROI.
- Build and refine Customer Lifetime Value (CLV) models to improve acquisition, retention, and customer segmentation strategies.
- Design, execute, and analyze A/B tests and quasi-experimental studies, including:
- Difference-in-Differences
- Synthetic Controls
- Geo Holdouts
- Propensity Scoring
- Instrumental Variables
- Evaluate the incremental impact of marketing initiatives through rigorous causal inference methodologies.
- Translate analytical findings into actionable recommendations for campaign targeting, creative strategy, and channel optimization.
- Partner with data scientists, machine learning engineers, and business stakeholders to deploy measurement solutions into production environments.
- Conduct exploratory research and develop innovative methodologies to better understand customer behavior and marketing effectiveness.
- Communicate complex technical concepts and research findings to both technical and non-technical audiences.
Required Qualifications- PhD, or Master's degree with 4+ years of experience in:
- Statistics
- Economics
- Computer Science
- Machine Learning
- Operations Research
- Related quantitative disciplines
- 3+ years of experience building machine learning models or developing analytical solutions for business applications.
- Strong experience with causal inference methodologies including:
- A/B Testing
- Difference-in-Differences
- Propensity Score Methods
- Instrumental Variables
- Synthetic Controls
- Experience with cloud computing platforms and distributed analytics environments.
- Strong programming skills in Python, R, or similar analytical languages.
Top Required Skills1. Causal Inference- Deep understanding of experimental and quasi-experimental methodologies used to measure incremental business impact.
2. Experiment Design- Experience designing, executing, and evaluating A/B tests and large-scale marketing experiments.
3. Cloud Computing- Experience leveraging cloud-based tools and platforms to support large-scale data science and modeling initiatives.
Preferred Qualifications- PhD in Statistics, Economics, Computer Science, Machine Learning, Operations Research, or a related field.
- 5+ years of experience building:
- Marketing Mix Models (MMM)
- Customer Lifetime Value (CLV) models
- Multi-Touch Attribution frameworks
- Experience designing and analyzing large-scale geo experiments.
- Publications or patents demonstrating advanced research expertise.
- Strong communication skills with the ability to present complex findings to leadership and business stakeholders.
Ideal Candidate BackgroundSuccessful candidates typically come from:
- Marketing Science
- Data Science
- Applied Economics
- Machine Learning
- Quantitative Research
- Advanced Analytics
Candidates should have a strong track record of using statistical methods and experimentation to solve complex business problems and drive measurable outcomes.
Ideal Candidate ProfileThe ideal candidate is equally comfortable conducting advanced statistical research and translating insights into business action. They bring strong expertise in marketing measurement, experimentation, and causal inference, with the ability to develop scalable analytical solutions that influence strategic decision-making across the organization.
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