About this role.
Reddit is seeking a Senior Staff Data Scientist to lead the scientific strategy for its ads measurement and signal systems. This high-visibility role focuses on advancing experimentation, causal inference, attribution, and privacy-aware measurement to prove advertiser value. The ideal candidate will define the technical vision, build trusted frameworks, and influence cross-functional teams and industry standards. As the principal architect, you will work at the center of Reddit's advertising ecosystem, connecting signals, identity, and ranking outcomes to drive performance. This is a unique opportunity to shape the future of measurement in a rapidly evolving privacy-first landscape.
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Cover letter sample
Dear Hiring Committee,
I am writing to express my strong interest in the Senior Staff Data Scientist - Ads Measurement, Signals, Privacy position at Reddit. With over a decade of experience in data science and a deep focus on causal inference, experimentation, and privacy-preserving measurement, I have led numerous initiatives that redefined how companies measure advertising effectiveness. My background includes building scalable lift measurement systems and designing privacy-safe methodologies that work within evolving regulatory and browser constraints.
I have a proven track record of setting technical strategy, influencing senior leadership, and working across engineering and product teams to deliver disruptive innovations. For example, I led the implementation of a conversion lift framework that improved customer-level measurement accuracy by 20% while reducing bias, directly impacting ad platform revenue. I am drawn to Reddit's mission-driven culture and its commitment to authentic communities, and I am excited about the opportunity to build trusted measurement at such a unique and influential platform.
Thank you for considering my application. I look forward to discussing how I can contribute to Reddit's ads measurement vision.
Sincerely,
Jane Doe
Sample interview questions
In a previous role, I designed a conversion lift study for a retail advertiser to measure the incremental lift of their display campaigns. The main challenge was contamination between test and control groups due to shared devices. I implemented a geo-based split and used device graph data to minimize cross-contamination. I also conducted a series of pre-tests to ensure the sample sizes were sufficient and the results were statistically significant. The study revealed that my original attribution model overstated performance by 30%, which led us to adopt more accurate measurement methods.
I would first categorize the measurement needs and identify which signals are affected by privacy changes. Then, I would design a hybrid approach combining first-party data, aggregated reporting, and modeled conversions. For instance, I would use Bayesian hierarchical models to estimate conversion rates from observed partial data, and apply differential privacy techniques to protect user-level information. I would also work closely with engineering to implement scalable infrastructure that can handle these models in real-time, and with policy teams to ensure compliance.
I once proposed replacing last-click attribution with a multi-touch attribution model. Many stakeholders were skeptical because the new model would shift budget allocations. I prepared a detailed presentation with simulated data and case studies showing that the new model better captured true customer journeys and would increase overall ROI by at least 15%. I also ran a pilot with one business unit to demonstrate the impact. By showing results a small group could understand, I gained the advocacy of a senior leader, which helped roll it out globally.
I would define key metrics such as prediction accuracy, coverage rate, and bias compared to underlying ground truth. Specifically, I would monitor the absolute error between modeled and observed values when possible, and use diagnostic tools like calibration plots. Guardrails would include a threshold for acceptable deviation, scheduled re-calibration when drift is detected, and a dashboard for real-time monitoring. I would also implement a validation process that periodically tests the model against a holdout set or via A/B tests with direct measurement.
I use a framework that aligns each project to the company's strategic goals and quantifies its potential impact on revenue, user value, or efficiency. I break down projects into clear milestones and use an an agile approach to cycle through discovery and delivery. For research-heavy projects, I set time-boxed exploration phases and make 'go/no-go' decision points. I also communicate early and often with stakeholders to manage expectations and re-allocate resources when a higher-priority initiative emerges.
Reddit’s Ads Data Science team is looking for a Senior Staff Data Scientist to lead the scientific strategy behind Reddit’s ads measurement and signal systems. This role sits at the center of how Reddit proves advertiser value, improves signal quality, builds privacy-aware measurement systems, and connects ads exposure to real advertiser outcomes.
As a Senior Staff Data Scientist for Ads Measurement, you will be the principal architect of our technical vision and the driving force behind the next generation of our measurement ecosystem. In a landscape rapidly shifting due to privacy regulations, browser changes, and evolving platform dynamics, you will spearhead innovation across experimental design (incrementality/lift), identity, signals, and privacy-safe 1P/3P measurement.
This is a high-visibility, high-impact role requiring a rare blend of deep experimentation & causal inference expertise, strategic foresight, and the ability to influence cross-functional executives and industry standards. You will not just adapt to the changing ad-tech environment, you will redefine how we measure value.
Responsibilities
- Technical Vision & Strategy: Define the long-term data science vision & strategy across ads measurement, signal quality, identity, attribution, and privacy. Establish how Reddit should evaluate advertiser value, measurement quality, and signal utility across first-party and third-party products.
- Set the Cross-Pillar Measurement Science Strategy: Define the long-term data science strategy across ads measurement, signal quality, identity, attribution, and privacy. Establish how Reddit should evaluate advertiser value, measurement quality, and signal utility across first-party and third-party products.
- Build Trusted Measurement and Evaluation Frameworks: Create rigorous frameworks for validating lift, attribution, identity quality, modeled conversions, signal loss recovery, and privacy-aware measurement. Define ground truth, objective functions, quality metrics, guardrails, and decision frameworks that guide product and engineering investments.
- Advance Experimentation and Causal Inference at Scale: Lead the evolution of Reddit’s experimentation and lift methodologies across Brand Lift, Conversion Lift, Split Testing, and emerging measurement products. Improve study quality, reduce bias and contamination, and develop scalable diagnostics for experiment health, feasibility, and interpretability. Partner with Ads Engineering to operationalize complex causal models, ensuring that scientific methodologies are not only accurate but also performant, scalable, and resilient in high-throughput production environments.
- Connect Signals, Identity, and Ranking Outcomes: Quantify how signal quality, match rates, identity resolution, modeled conversions, and privacy changes affect bidding efficiency, CPA, ROAS, and advertiser outcomes. Partner with modeling and ranking teams to translate measurement improvements into performance gains.
- Guide Privacy-Aware Ads Measurement: Lead a privacy-first measurement paradigm, positioning Reddit as a market leader in trusted advertising by recovering signal utility through compliant modeling.
- Create Durable Data Science Infrastructure and Standards: Lead cross-org efforts to define reusable methodologies, dashboards, scorecards, quality metrics, and best practices. Build repeatable systems that improve how Ads DS evaluates launches, monitors regressions, sizes opportunities, and communicates impact.
- Influence Senior Cross-Functional Strategy: Partner with senior leaders across Product, Engineering, Sales, Marketing Science, Legal/Privacy, and Ads leadership to shape roadmap decisions. Translate complex scientific tradeoffs into clear business and product recommendations.
- Uplevel the Data Science Organization: Mentor Staff and Senior data scientists, sponsor high-impact technical work, and raise the bar for causal inference, measurement science, identity evaluation, data quality, and cross-functional decision-making across Ads DS.
Qualifications
Required
- Advanced degree in Statistics, Economics, Mathematics, Computer Science, Operations Research, Physics, or a related quantitative field, or equivalent industry experience.
- 10+ years of industry experience in data science, applied science, economics, statistics, or a related quantitative role.
- Deep expertise in ads measurement, experimentation, causal inference, attribution, marketplace measurement, or ads optimization.
- Proven track record leading ambiguous, cross-functional, multi-pillar problem spaces with measurable business impact.
- Strong command of statistical modeling, experimental design, causal inference, and measurement methodology.
- Experience defining metrics, evaluation frameworks, quality guardrails, and decision systems for complex products.
- Demonstrated ability to balance long-term strategic vision with hands-on execution of complex technical ideas.
- Advanced proficiency in SQL and Python or R.
- Ability to influence senior product, engineering, and business leaders through clear technical judgment and communication.
- Demonstrated ability to mentor senior ICs and improve technical standards across a data science organization.
- Demonstrated agility in adopting AI tools to amplify your personal output, turning complex methodologies into working prototypes with modern speed and efficiency.
Preferred
- Experience with ads identity, conversion modeling, signal loss, modeled conversions, match-rate optimization, or identity graph evaluation.
- Experience with lift measurement, brand lift, conversion lift, incrementality testing, MMM, MTA, or third-party measurement partnerships.
- Experience working on privacy-constrained measurement, clean rooms, aggregation, consent-aware systems, or privacy-preserving modeling.
- Experience partnering with ranking, bidding, or machine learning teams to connect measurement quality to optimization outcomes.
- Familiarity with two-sided marketplaces, auction systems, performance advertising, or advertiser-facing measurement products.
- Experience building durable internal frameworks, training programs, scorecards, or methodology standards adopted across an organization.
Benefits:
- Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
- Family Planning Support
- Gender-Affirming Care
- Mental Health & Coaching Benefits
- Comprehensive Medical Benefits & Health Care Spending Account
- Registered Retirement Savings Plan with matching contributions
- Income Replacement Programs
- Flexible Vacation & Paid Volunteer Time Off
- Generous Paid Parental Leave
#LI-Remote
In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews.
During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable. We will not sell your personal information or disclose it to any third party for their marketing purposes. We will delete any recording of your interview promptly after making a hiring decision. For more information about how we will handle your personal information, including our retention of it, please refer to our Candidate Privacy Policy for Potential Employees and Contractors.
Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve. Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.
Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›
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