Lead Marketing Data Scientist

Key Responsibilities

• Data Architecture & Integration
• Design, streamline, and integrate data pipelines between marketing platforms and the business data warehouse.
• Build and maintain a unified marketing data layer connecting impressions, clicks, and engagement signals to downstream conversion, funding, and revenue events.
• Partner with Data Engineering to define schemas, ingestion patterns, and data quality standards ensuring trusted, decision-grade marketing data.
• Identify and eliminate gaps, latency, and inconsistencies between platform-reported metrics and business outcomes.

Marketing Mix Modeling & Scenario Analysis

• Develop, productionize, and own the company's Marketing Mix Modeling (MMM) framework across paid, owned, and earned channels.
• Run scenario analyses and budget optimization simulations to recommend optimal media mix under varying spend, seasonality, and macroeconomic conditions.
• Quantify diminishing returns, saturation curves, carryover effects, and channel interactions to inform short- and long-term planning.
• Translate model outputs into clear, actionable recommendations for marketing leadership and the executive team.

Incrementality, Geo-Lift & Experimentation

• Design and execute geo-lift studies, holdout tests, and matched-market experiments to measure true incremental impact by channel and campaign.
• Establish a rigorous experimentation roadmap and testing cadence across the media portfolio, including paid social, paid search, display, CTV, audio, and direct mail.
• Reconcile platform-attributed performance with incrementality results and educate stakeholders on the difference between correlation and causal impact.

Performance Insights & Business Impact

• Correlate impression- and click-level signals with funded loans, revenue, LTV, and other core business KPIs.
• Build self-serve dashboards and analytical products that help the marketing organization make confident, data-driven media mix decisions.
• Surface growth opportunities, efficiency gains, and underperforming investments through proactive analysis and storytelling.
• Set and track measurement standards, attribution conventions, and success metrics across the marketing organization.

Leadership & Cross-Functional Partnership

• Serve as the technical and analytical thought leader for marketing measurement across the company.
• Closely partner with Performance Marketing, Brand, Finance, Product, and Data Engineering teams to align measurement with business strategy.
• Mentor analysts and junior data scientists, and help shape the long-term marketing analytics roadmap and team structure.
• Communicate complex statistical concepts and trade-offs clearly to non-technical executives and channel owners.

Requirements

• 7+ years of experience in data science, marketing analytics, or quantitative marketing, with at least 3 years focused on performance marketing measurement.
• Proven track record of building and deploying Marketing Mix Models (Bayesian or frequentist).
• Experience with AI/ML based LTV prediction to build predictive models that help achieve business outcomes.
• Strong programming skills in Python (pandas, NumPy, scikit-learn, statsmodels, PyMC / Stan) and SQL; comfortable working with large-scale datasets.
• Hands-on experience designing and analyzing incrementality tests, geo-lift studies, and causal inference experiments.
• Experience with at least one major cloud data platform (Snowflake, BigQuery, Databricks, Redshift) and modern data stack tooling (dbt, Airflow, Fivetran, or equivalent).
• Strong working knowledge of digital media and performance platforms including Google Ads, Meta Ads, programmatic DSPs, affiliate platforms, call tracking, and attribution tools.
• Experience operating in a SaaS, fintech, financial services, or other fast-paced, high-growth, performance-driven environment.
• Excellent communication and storytelling skills with the ability to influence senior stakeholders and translate analytics into business action.

Preferred Qualifications

• Bachelor's degree in a quantitative discipline (Statistics, Economics, Mathematics, Computer Science, Engineering, or related field).
• Master's or Ph.D. in Statistics, Econometrics, Data Science, or related quantitative field.
• Experience in financial services, lending, small business finance, or other regulated industries.
• Familiarity with privacy-aware measurement approaches (data clean rooms, conversion APIs, server-side tracking, MMM in a post-cookie world).
• Experience integrating LTV, retention, and unit-economics models into marketing measurement frameworks.
• Exposure to visualization tools such as Looker, Tableau, Power BI, or Streamlit for analytical product delivery.
• Prior experience leading or mentoring a team of analysts or data scientists.