• lead the development of machine learning (ML) methods and analyses of patient data • perform project-specific hands-on analysis and modeling of high-dimensional longitudinal real-world data • contribute to the design, implementation, and evaluation of innovative machine learning approaches • articulate and break down large problems into solvable pieces • participate in code reviews and provide regular updates on work

• Set Long-Term Marketing Strategy: Define the 2–3 year technical roadmap for marketing measurement. Establish the "North Star" metrics and frameworks that determine how Reddit allocates hundreds of millions in marketing budget. • Optimize Marketing ROI: Build and refine Media Mix Models (MMM) and Multi-Touch Attribution (MTA) systems to mathematically quantify the incremental impact of marketing spend. • Bridge B2B & B2C Growth: Design unified frameworks to optimize marketing aimed at both new advertisers (driving revenue) and new users (driving engagement), identifying synergies where brand awareness for one fuels growth for the other. • Advance Causal Inference & Experimentation: Lead the design of complex "always-on" incrementality testing and geo-holdout experiments. Develop methodologies to measure the long-term "halo effect" of brand marketing on organic growth. • Lead Through Influence: Collaborate deeply with Marketing, Growth, and Finance to translate complex data insights into actionable budget shifts. • Be the Technical Guide for the Team: Mentor junior scientists and set the technical bar for code quality and statistical rigor across the Marketing organization.

• Lead and Develop: Build, lead, and mentor a high-performing team of data scientists focused on the Marketing & Business Development domain. • Drive Strategy: Serve as a thought-partner to cross-functional leaders (Product, Engineering, Design, and Marketing), communicating and shaping the roadmap and strategy for Reddit by identifying actionable and impactful insights. • Deliver Causal Insights: Lead initiatives to quantify the true incremental lift of marketing spend and prevent budget allocation on non-incremental channels, including developing and deploying Incremental/Predictive LTV models. • Standardize Measurement: Oversee the establishment of measurement frameworks (incl. effective experimental designs and/or Causal inference methods) to evaluate Brand Initiatives and ensure consistent metrics are aligned across the organization for campaign quality and ROI. • Drive Adoption: Drive the adoption of strategic and tactical recommendations based on deep product experience and data understanding to maximize our value to consumers. • Ensure Data Quality: Take a keen interest in the collection and quality of underlying data, including overseeing work on ETLs, reporting dashboards, and data aggregations needed for business tracking and/or ML model development. • Enable Self-Service: Lead initiatives to enable analytics self-serve capabilities for cross-functional teams.

Role Description We are seeking a Senior Data Scientist who combines strong expertise in statistical analysis and applied machine learning with practical experience in production-oriented workflows. This role is not just about building models in isolation—you will partner closely with Senior Machine Learning Engineers to ensure models are production-ready, monitored, and continuously improved. You’ll focus on generating insights, developing models, and proactively collaborating in monitoring deployed algorithms. Beyond the technical, you’ll help identify opportunities for model-driven enhancements and business and customer recommendations based on real-world performance. Key Responsibilities - Statistical & ML Modeling - Develop, validate, and tune statistical and machine learning models that solve complex business problems. - Partner with engineers to ensure models are designed with production deployment in mind. - Design experiments and evaluate models using robust statistical methodologies. - Build and maintain dashboards that proactively monitor product efficacy and distribute key insights across product and customer domains. - Production Awareness & Monitoring - Collaborate with ML engineers on deployment pipelines, APIs, and infrastructure. - Proactively monitor deployed models for drift, accuracy, and reliability. - Provide insights and business recommendations based on model performance in production. - Recommend retraining or refinement strategies in response to performance changes. - Business Impact & Strategy - Translate model results into actionable recommendations for both product and business teams. - Identify opportunities for model improvements that drive up-sell, revenue growth, and cost reduction. - Communicate results clearly to both technical and non-technical stakeholders. - Collaboration & Leadership - Work side-by-side with Senior Machine Learning Engineers to ensure smooth handoff from research to deployment. - Mentor team in best practices for applied ML and production readiness. - Contribute to evolving data science standards and playbooks that prioritize operational impact. Qualifications - 5+ years of experience applying machine learning and statistics to business problems. - Master’s or PhD in Statistics, Computer Science, Data Science, or related field (or equivalent experience). - Prior exposure to production ML environments and workflows. Technical Skills - Strong proficiency in Python (pandas, scikit-learn, PyTorch/TensorFlow). - Strong background in statistical methods (supervised/unsupervised learning, classification models, etc.), experimental design, and data visualization (Looker) and storytelling. - Strong proficiency in SQL (Snowflake) with an ability to work with raw data and collaborate with data engineering teams to optimize data pipelines. - Proficiency with cloud platforms (AWS). - Solid understanding of APIs, model deployment processes, and monitoring practices. - Familiarity with other programming languages such as R, Java, and Go. Soft Skills - Excellent communication skills with ability to bridge technical and business contexts. - Strong problem-solving and proactive ownership mindset. - Comfort working in cross-functional teams with engineers, product managers, and business leaders. - Ability to balance quick iterations with building long-term scalable solutions. Benefits - Competitive compensation and benefits. - A high-impact environment and data-driven culture where your models make it into production and drive measurable business outcomes. - Opportunities to shape both data science practices and production ML systems. - Competitive salaries & Bonus Plan (for eligible roles) and Equity Plan. - Modern Health for financial, mental, and physical wellness. - 401(k) Retirement Plan & Match (US Offices) and Local Country Pension (International Offices). - Unlimited Vacation and Flexible hours. - Comprehensive medical benefits for you and your family ❤️. - Emotional & Physical Wellness – Access to wellness services (EAP & Prove Well-Being Reimbursement). - Bottomless snacks & beverages for certain office locations. - Daily GrubHub stipend for lunch if coming into the office (US Offices). - A great place to work and connect with other talented Provers like yourself!