Data Scientist - Data Strategy & Insights (DSI) at Lunar Solar Group

Our clients invest heavily in paid media, and they all want to know the same thing: what is actually driving growth? As our Data Scientist, you will help answer that question. Partnering closely with our Data Strategist, you will build out the measurement program within Lunar Solar Group's Data Strategy & Insights (DSI) team, bringing multi-touch attribution, incrementality testing, and media mix modeling together into clear, defensible recommendations that help our clients decide how to invest.

You will report to the Sr. Director of Data Strategy, work day-to-day with our media buyers, strategists, and account leads, and present your findings directly to client stakeholders. You will work alongside a Data Strategist to plan the right measurement path for each client, design tests and models, and present results to internal teams and clients. The statistical rigor behind our modeling framework and experiment planning is yours to own; that is the expertise this role adds to the team. Together you turn it into data-backed recommendations with caveats clearly stated. Our Client Strategy team helps shape how those recommendations are communicated, and our clients make the final budget decisions.

Success in this role looks like a steady cadence of well-designed incrementality tests, calibrated media mix models running for our priority clients, and recommendations that clients trust enough to use when they make budget decisions.

Key Responsibilities:

• Own the statistical rigor of DSI's measurement framework: model specification, assumptions, validation, and diagnostics for media mix and attribution models, plus the design standards (power analysis, sample sizing, test duration, and success criteria) behind every experiment.
• Work with the Data Strategist to plan the right measurement path for each client, then design, launch, and read out incrementality tests across Meta, Google, and other paid channels, including power analysis before every launch.
• Build, calibrate, and refresh media mix models for priority clients, using lift test results to ground the models, and deliver response curves and budget scenarios.
• Evaluate multi-touch attribution outputs, audit third-party measurement tools, and set clear guidelines for when attribution should and should not drive decisions.
• Reconcile attribution, experiments, and modeling into one point of view on channel value for each client, and explain why the sources disagree when they do.
• Present findings alongside the Data Strategist to clients and internal teams in plain language, with a clear recommendation, honest confidence levels, and clearly stated caveats.
• Clean, validate, and join data from ad platforms, web analytics, and commerce systems before any modeling begins.
• Turn repeat work into reusable code, templates, and a shared testing calendar so measurement scales across our client roster.
• Help media buyers and strategists build their own measurement fluency.

Skills & Qualifications:

• 3+ years of experience in applied data science or marketing analytics, including at least 2 years hands-on in marketing measurement.
• Direct experience regularly running at least one of the following on real ad spend, with working knowledge of the other two: media mix modeling, incrementality testing, or multi-touch attribution.
• Strong grounding in causal inference, including experimental design, power analysis, synthetic control, and difference in differences.
• Experience building media mix models, including adstock, saturation, and calibration with experiment results, using a framework such as Google Meridian, Meta Robyn, or PyMC Marketing.
• A clear understanding of where attribution breaks down, including walled gardens, signal loss, and view-through credit.
• Proficiency in Python (pandas, statsmodels, scikit-learn) and SQL.
• Ability to explain statistical results to non-technical audiences, in writing and in live client conversations.
• Comfort managing several clients or projects at once and owning ambiguous questions from scoping through delivery.
• Willingness to deliver unwelcome findings, backed by evidence, when the data calls for it.

Preferred:

• Agency experience supporting multiple clients with different levels of data maturity.
• Experience with experiment tools such as GeoLift or CausalImpact.
• Experience with a cloud data warehouse (BigQuery or Snowflake) and Git.
• Experience with Meta Ads, Google Ads, GA4, and Shopify data.
• Experience with measurement for Amazon, retail, or offline channels.
• Familiarity with third-party measurement platforms such as Northbeam, Triple Whale, Rockerbox, or Haus.
• Working knowledge of R.
• Dashboarding experience in Looker Studio, Tableau, or similar.
• A degree in statistics, economics, data science, or a related quantitative field, or equivalent practical experience.