> Markdown version of [/jobs/ext/3038304-director-data-science](https://www.wearedevelopers.com/jobs/ext/3038304-director-data-science). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Director, Data Science - **Company:** reddit Inc. - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $317,000.0 - **Contract:** Permanent contract - **Skills:** Machine Learning, Data Management - **Published:** September 23, 2026 - **Apply:** https://job-boards.greenhouse.io/reddit/jobs/8226397 ## About the Role * Deep data science expertise in measurement, experimentation, causal inference, ML, data platforms, and analytical systems, with the technical judgment to set company-scale standards. * Experience in UGC or community ecosystems and a strong point of view on how data science can improve consumer experiences while protecting analytical quality, privacy, and responsible AI practices. * Proven ability to connect model and system improvements to consumer value, content quality, trust, diversity, healthy participation, and long-term business outcomes-not only short-term engagement metrics. * Senior leadership experience leading multi-team or multi-pillar organizations and delivering attributable business impact through durable roadmaps, operating mechanisms, and long-range bets. * Proven organization builder with a track record of recruiting and retaining exceptional senior leaders, developing Directors and managers, and creating a healthy, high-trust leadership bench. * Exceptional communication and influence with experience aligning senior product, engineering, and business leaders; resolving conflict; and communicating uncertainty, risks, and tradeoffs with intellectual honesty. ## Description * Partner with senior leaders across Consumer, Product, Engineering, and business functions to define company-level bets, priorities, and tradeoffs. * Set the data science strategy for Reddit's feed, recommendation, search, retrieval, and ranking systems, including objective design, candidate generation, ranking quality, relevance evaluation, experimentation, and long-term optimization. * Establish the measurement and experimentation framework for relevance optimization, including offline evaluation, online experimentation, counterfactual analysis, guardrail metrics, metric integrity, and the connection between model improvements and consumer and community outcomes. * Translate complex user and community behavior into clear, impartial insights that improve product strategy and consumer outcomes. * Establish durable standards for measurement, experimentation, causal inference, reproducibility, privacy, responsible AI use, and self-serve measurement, at scale. * Build multi-team roadmaps, operating cadences, and clear accountability that turn company direction into durable delivery. * Exercise strong technical judgment across architecture choices, analytical systems, hiring, and sponsorship of high-leverage programs. * Build or rebuild systems and practices that make analytical work reliable, adoptable, and scalable rather than dependent on individual heroics. * Recruit, retain, and develop exceptional Directors, managers, and IC5+ leaders across multiple teams or pillars. * Set a high talent bar through succession planning, calibration, and clear expectations for coaching, performance, accountability, and growth. * Create durable cross-org forums and operating mechanisms that improve coordination, culture, and decision quality. * Align senior product, engineering, and business leaders on measurement strategy, difficult tradeoffs, and decisions with material multi-year impact. * Represent Reddit externally and internally as an acknowledged thought leader and visible culture shaper for rigor. * Partner with teams advancing ML and analytical systems that deepen understanding of Reddit's UGC and community ecosystem. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [It's all about the Data](https://www.wearedevelopers.com/videos/425-it-s-all-about-the-data) - [Data: The Deciding Factor in AI Success](https://www.wearedevelopers.com/videos/100310-data-the-deciding-factor-in-ai-success) - [What non-automotive Machine Learning projects can learn from automotive Machine Learning projects](https://www.wearedevelopers.com/videos/397-what-non-automotive-machine-learning-projects-can-learn-from-automotive-machine-learning-projects) - [The Data Mesh as the end of the Datalake as we know it](https://www.wearedevelopers.com/videos/156-the-data-mesh-as-the-end-of-the-datalake-as-we-know-it) ## Related Articles - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Dev Digest 137 - AI'm not sure about this](https://www.wearedevelopers.com/magazine/485-dev-digest-137-ai-m-not-sure-about-this) - [Dev Digest 129 - Now that's what I call private data!](https://www.wearedevelopers.com/magazine/468-dev-digest-129-now-that-s-what-i-call-private-data) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Panel Discussion: Responsible AI in Practice - Real-World Examples and Challenges](https://www.wearedevelopers.com/magazine/488-panel-discussion-responsible-ai-in-practice-real-world-examples-and-challenges)