Applied Statistician, GTM Analytics & Market Intelligence - Industry Operations - NA

Job Description: The Applied Statistician will apply statistical theory, quantitative modeling, and computational methods to analyze advertising, sales, campaign, client portfolio, and market datasets. The role will support GTM and Branding Ads decisions by designing statistical analyses, building forecasting and ROI models, evaluating program performance, developing statistically sound reporting frameworks, and communicating quantitative findings and limitations to technical and non-technical stakeholders.

Responsibilities

• Statistical Modeling & Analysis: Apply advanced statistical methods (regression, causal inference, cohort analysis, and forecasting) to evaluate GTM campaigns, product adoption, and market outcomes.
• Experimental Design & Validation: Design statistical analysis plans, assess data limitations/uncertainty, and validate the robustness of findings before translating them into business recommendations.
• Market Intelligence & Opportunity Sizing: Analyze vertical-level datasets to develop scoring frameworks for client health, account opportunity sizing, and resource allocation.
• Data Engineering & Reproducibility: Clean, manipulate, and analyze large structured datasets using SQL, Python, or R, while maintaining reproducible data workflows and rigorous QA checks.
• Business Intelligence & Reporting: Design dashboards and tracking frameworks with statistically consistent KPI definitions to monitor revenue mix, sales funnels, and program health.
• ROI Modeling & Program Evaluation: Build statistical business cases and conduct pre/post-mortem evaluations to measure the effectiveness and ROI of regional initiatives and product launches.
• Cross-Functional Communication: Translate complex statistical findings into clear visualizations and executive-ready narratives for Sales, Product, and Leadership stakeholders.

Minimum Qualifications

• Education: Bachelor’s degree in Statistics, Mathematics, Economics, Data Analytics, or a highly quantitative field (or equivalent practical experience).
• Data Manipulation: Advanced proficiency in SQL and statistical programming languages (Python or R) for cleaning and analyzing large, structured datasets.
• Statistical Expertise: Professional-level knowledge of core statistical concepts, including regression models, hypothesis testing, confidence intervals, and multivariate analysis.
• Business Intelligence: Experience designing data workflows, dashboards, and KPI tracking frameworks using BI tools (e.g., Tableau, PowerBI) and Excel.
• Experimental Design: Hands-on experience with A/B testing, experimental/quasi-experimental design, or pre/post-program evaluation methodologies.
• Analytical Communication: Proven ability to translate complex data and statistical frameworks into clear, actionable business insights for non-technical stakeholders.

Preferred Qualifications

• Advanced Degree: Master’s or Ph.D. in a quantitative discipline (e.g., Applied Statistics, Econometrics, Data Science).
• Domain Experience: Experience applying quantitative analysis to Go-To-Market (GTM) strategies, digital advertising, branding ads, or client portfolio management.
• Advanced Modeling: Expertise in causal inference, incrementality analysis, sensitivity analysis, and predictive revenue/market forecasting.
• Data Pipelines: Familiarity with maintaining reproducible data pipelines, automated QA checks, and methodologies at scale.
• Strategic Sizing: Experience building statistical scoring frameworks for vertical prioritization, market potential, and resource allocation.
• Stakeholder Management: Demonstrated success advising cross-functional leadership (Product, Marketing, Sales) and leading regional enablement initiatives.