5+ years in Marketing Science, Quantitative Marketing, Data Science, or Econometrics, preferably in a high-growth agency or adtech environment.
Degree in a quantitative field such as Statistics, Econometrics, Applied Mathematics, or Computer Science.
Expertise in Marketing Mix Modeling (MMM) and Bayesian statistics.
Advanced skills in Python or R for data manipulation and statistical modeling.
Strong SQL proficiency for querying complex databases.
Responsibilities
Architect and validate Marketing Mix Models to assess media effectiveness.
Design and execute incrementality frameworks, including randomized controlled trials.
Drive methodology for integrating MMM outputs with attribution models.
Build forecasting algorithms and lifetime value models for budget optimization.
Develop scalable modeling pipelines in Python or R with high code quality standards.
Collaborate with data engineers to integrate client data with marketing stacks.
Translate complex quantitative outputs into actionable budget reallocations.
Benefits
Opportunity to lead innovative marketing science projects.
Mentorship and professional development in statistical theory and experimental design.
Access to cutting-edge tools and technologies in marketing analytics.
Collaborative work environment with data engineers and client strategy leads.
Engagement in thought leadership and staying current with marketing science research.
Full Job Description
About the role
We are looking for an applied quantitative practitioner to elevate our agency's marketing science capabilities. In this role, you will lead the development and execution of sophisticated statistical models, design custom experimentation agendas, and translate complex algorithmic outputs into high-impact growth strategies.
This role's core focus is scientific rigor, methodology design, and delivering validated incrementality models that directly inform client capital allocation.
What you'll do
Measurement Science & Statistical Modeling
Architect, validate, and scale Bayesian/frequentist Marketing Mix Models (MMM) to establish true omni-channel media effectiveness and marginal ROAS curves.
Design, execute, and evaluate incrementality frameworks, including Matched-Market/Geo-Experiments, Synthetic Control Methods, and randomized controlled trials (RCTs).
Drive methodology for triangulating MMM outputs, attribution models, and experimental results into unified target response curves.
Build stochastic forecasting algorithms and lifetime value (LTV) models to optimize budget allocation across portfolio channels.
Technical Innovation & Analytics Infrastructure
Develop reproducible, scalable modeling pipelines in Python or R, maintaining high standards for code quality, version control (Git), and data governance.
Custom-build adstock, saturation, and decay functions tailored to unique client buying cycles and macro-economic signals.
Collaborate with data engineers to integrate client data warehouses (Snowflake, BigQuery) with Relay, Mile Marker's open-powered marketing stack.
Strategic Consulting & Commercial Storytelling
Bridge the gap between complex quantitative outputs and executive decision-making-translating statistical outputs into clear, actionable budget reallocations.
Partner with client strategy leads to draft data-driven Learning Agendas that systematically test incrementality hypotheses.
Serve as the subject matter expert in client meetings and pitch presentations, building credibility through methodological clarity and mathematical rigor.
Thought Leadership & Mentorship
Mentor Analysts and Data Scientists in statistical theory, experimental design, Python/R best practices, and model interpretation.
Stay at the forefront of marketing science research, continually testing new open-source libraries, causal frameworks, and AI/ML applications in media.
Qualifications
Experience
5+ years of professional experience in Marketing Science, Quantitative Marketing, Data Science, or Econometrics, ideally within a high-growth agency, adtech, or direct-to-consumer environment.
Degree in a quantitative discipline (Statistics, Econometrics, Applied Mathematics, Computer Science, Economics, or related quantitative field) preferred.
Technical Requirements
Expert-level mastery of Marketing Mix Modeling (MMM), Bayesian statistics, time-series analysis, and causal inference techniques (Matched Markets, Synthetic Controls, Difference-in-Differences).
Advanced proficiency in Python (Pandas, NumPy, PyMC, Statsmodels) or R (Tidyverse, Prophet, CausalImpact) for data manipulation, statistical modeling, and automation.
Strong proficiency in SQL for querying complex relational databases and cloud platforms (BigQuery, Snowflake, Redshift).
Standard workflows using Git/GitHub.
Ability to build programmatic visualizations and clear analytical dashboards using tools like Streamlit, Dash, Tableau, or Looker.
Business & Communication
Demonstrated ability to explain complex statistical models, confidence intervals, and incrementality tests to non-technical client executives.
Proven track record of translating model outputs into clear budget optimization strategies that increase client ROI and enterprise value.
