Job Description
We’re seeking a part-time Media Mix Modeler who is currently pursuing an advanced degree in Statistics, Mathematics, Econometrics, Data Science, or a related quantitative field. In this role, you will apply Bayesian time series modeling and causal inference techniques to build, evaluate, and optimize Media Mix Models (MMM) using real-world client data. You’ll work across multiple modeling frameworks — including Meridian, Robyn, Orbit KTR, and Slingwave’s proprietary R packages — to develop robust models that quantify media impact, forecast outcomes, and guide optimal budget allocation decisions. This is a high-impact analytical role where your models will directly influence marketing investment strategies for leading brands. You’ll collaborate closely with Slingwave’s analytics and customer teams to translate complex statistical outputs into actionable recommendations that drive measurable business results.
Key Responsibilities
Build Advanced Media Mix Models: Develop Bayesian MMM models to predict revenue, conversions, and other KPIs. Incorporate media spend, seasonality, promotions, and other exogenous variables
Evaluate Modeling Approaches: Compare and assess methodologies across Meridian, Robyn, Slingwave’s framework, Orbit KTR, and related tools. Identify strengths, limitations, and best-fit applications for different client scenarios
Drive Budget Optimization: Translate model outputs into optimal cross-channel budget allocation recommendations. Analyze diminishing returns, saturation curves, and marginal ROAS
Develop Forecasting & Scenario Planning Tools: Create simulations to project performance under various spend scenarios. Support forward-looking media planning decisions
Design Clear Visualizations: Develop dashboards, visualizations, and/or lightweight user interfaces/ Clearly communicate media contribution, forecasting, and optimization insights
Collaborate Cross-Functionally: Partner with analytics and customer-facing teams. Ensure modeling outputs align with business objectives and client needs
Required Skills
Advanced Quantitative Training: Currently pursuing (or recently completed) a Master’s or PhD in Statistics, Mathematics, Econometrics, Data Science, or related field
Strong Bayesian & Time Series Foundations: Experience with Bayesian modeling, hierarchical models, state-space models, or causal inference
MMM & Marketing Analytics Exposure (Preferred): Familiarity with Meridian, Robyn, Orbit KTR, or similar MMM frameworks
Technical Proficiency: Strong programming skills in R (required). Experience with Python, Stan, PyMC, or probabilistic programming frameworks is a plus.
Experimental & Causal Thinking: Understanding of marketing effectiveness measurement, attribution, and experimentation.
Data Visualization Skills: Ability to translate complex outputs into intuitive visualizations and business-ready narratives.
Strong Communication Skills: Capable of explaining statistical findings to both technical and non-technical stakeholders.
Self-Directed & Organized: Comfortable working remotely across time zones. Strong project management and independent execution skills.
Position Details
Apply advanced Bayesian modeling to real-world marketing investment decisions
Influence how leading brands allocate millions in media spend
Work with cutting-edge MMM frameworks and AI-driven proprietary tools
Gain exposure to applied causal inference and budget optimization at scale
Flexible, part-time contractor engagement
Remote-first collaboration across NYC, MN, CA, and nationwide
Competitive contractor compensation commensurate with experience
If you’re excited to apply advanced statistical modeling to real marketing decisions — and want your work to directly shape how brands optimize spend in an AI-driven world — we’d love to hear from you.
Company Overview
Slingwave empowers top eCommerce, D2C, and CPG brands with best-in-class AI analytics-driven media measurement and optimization. Our suite — MMM+ (Media Mix Modeling), VELOCITY AMA (Agile Marketing Attribution), SAGE Experimentation, and Slingshot AI — combines advanced analytics, rigorous experimentation, and machine learning to deliver AI-powered optimization of media spend across Google/YouTube, Meta, Amazon, emerging digital channels, and beyond.
Backed by seasoned industry operators, we’re scaling rapidly and are looking for a part-time Media Mix Modeler to help build and advance our next generation of Bayesian measurement solutions.
