Senior Data Scientist

About the role

You will architect and lead advanced causal analysis initiatives that translate complex observational data into clear, action-oriented strategies for Reddit's product direction. In this position, you will own the end-to-end lifecycle of experimentation insight, guiding test design, execution, and meta-analysis to maximize learning across the platform. You will build and maintain the data infrastructure necessary for self-serve analysis, creating ETL pipelines, tables, and dashboards that empower both data scientists and business partners. You will define and monitor team-level KPIs, enabling data-driven quarterly goal setting and rigorously measuring the impact of Reddit's largest communities. A core part of this role involves mentoring junior analysts and business stakeholders, elevating data literacy and ensuring data science methods directly inform high-stakes decisions. You will communicate results directly to executive leadership, ensuring recommendations are compelling, clear, and actionable up to the C-suite. Full-time telecommuting is an option for this position.

Key facts

What you'll do

Design and execute observational causal studies that isolate true platform drivers and quantify business impact for strategic decisions.

Lead meta-analyses of existing experiments to synthesize learnings, identify patterns, and redefine future test priorities at the highest level.

Develop scalable ETL pipelines and robust data tables that serve as the foundation for reliable, self-serve analytics.

Construct intuitive dashboards and custom data tools that allow non-technical partners to explore metrics and validate hypotheses independently.

Establish and refine team-level KPIs, translating abstract goals into measurable outcomes that demonstrate the value of data initiatives.

Partner deeply with cross-functional stakeholders to gather requirements, align on success metrics, and ensure data insights support business objectives.

Mentor internal data scientists and business leaders, coaching them on best practices for analysis, experimentation, and data interpretation.

Own the data visualization strategy for major programs, ensuring reporting is transparent, accurate, and accessible to executive audiences.

Implement rigorous data quality checks and monitoring systems to safeguard the integrity of analyses used for high-impact product decisions.

Champion the adoption of advanced statistical methods across the organization, pushing the boundaries of what the team can learn from its data.

Define and maintain documentation standards for models, pipelines, and experiments to ensure continuity and reproducibility of results.

Evaluate emerging data infrastructure opportunities, recommending investments that improve speed, scale, and reliability for analytical workloads.

Synthesize findings from fragmented data sources into cohesive narratives that clarify trade-offs and guide long-term product vision.

Champion an experimentation culture by removing barriers to entry and enabling any team to run high-quality tests efficiently.

Regularly present insights and recommendations to senior leadership, translating technical analysis into strategic guidance.

Requirements

Master's degree in Data Science, Statistics, Machine Learning, Operations Research, Economics, Computer Science, Engineering (any field), or a related quantitative discipline and two (2) years of experience in the job offered or in any occupation in related field.

Bachelor's degree in Data Science, Statistics, Machine Learning, Operations Research, Economics, Computer Science, Engineering (any field), or a related quantitative discipline and five (5) years of progressively responsible experience in the job offered or in any occupation in related field.

Demonstrated expertise in A/B testing and experimentation design, including hypothesis formulation, sample size calculation, and result interpretation.

Proven ability to conduct causal inference and incrementality measurement using observational and experimental data.

Strong experience with statistical modeling and exploratory data analysis across large, complex datasets.

Hands-on capability in ETL development and maintenance using modern data tools and workflows.

Expertise in designing and implementing KPI frameworks, including goal setting, tracking, and impact analysis.

History of successful cross-functional collaboration in fast-paced, matrixed environments.

Advanced skills in data visualization, including the creation of clear reports and interactive dashboards.

Proficiency in Python and SQL for data manipulation, analysis, and prototyping.

Experience applying machine learning and predictive modeling techniques to real-world business problems.

Ability to translate ambiguous business problems into well-defined analytical approaches and success metrics.

Comfortable working with incomplete or noisy data and drawing defensible conclusions under uncertainty.

Excellent written and verbal communication skills for influencing stakeholders at all levels.

Practical notes

Full-time telecommuting is an option.

No travel is required.

No visa sponsorship is provided.

No application deadline is specified.