Engineering Director- Ads Measurement

At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team. LinkedIn Marketing Solutions (LMS) is the largest and fastest growing B2B Advertising Platform at scale in the world, with revenue north of $6B+. We help organizations grow. The LMS engineering team is responsible for building and scaling the technology stack that powers our advertising business including advertiser experiences, ad serving platform and ad products. Lead product measurement for LinkedIn Marketing Solutions (LMS) Ads Measurement, owning experimentation, incrementality, and attribution strategy and execution across ad products. Partner closely with Product Management, Engineering, Data Science, and Privacy to deliver rigorous, privacy-first measurement that drives advertiser outcomes and product adoption. Top Outcomes Establish a company-standard experimentation and incrementality framework used across LMS AMO Improve decision quality and speed for product launches and iterations (clear guardrails, power analysis, confidence intervals) Deliver resilient attribution signals that align with privacy constraints and drive better decisioning Ship measurement features and APIs that increase measurable lift and advertiser trust Build and retain a high-performing measurement team with clear operating mechanisms Responsibilities Own end-to-end measurement strategy for LMS AMO: hypothesis development, experiment design, lift estimation, and causal inference Define and scale experimentation guardrails: randomized holdouts, multi-cell designs, geo tests, sequential testing, and power/sample-size planning Lead incrementality measurement across funnel stages (including BOFU), quantifying true lift for conversions, leads, and revenue outcomes Architect privacy-aware attribution approaches (event-level, conversions windows, view-through policy, path-based and data-driven methods) with robust uncertainty estimates Partner with PM/Eng to make measurement a product capability: telemetry requirements, experiment platforms, APIs, dashboards, and reviewer workflows Establish canonical reporting, confidence intervals, and decision thresholds to reduce ambiguity and prevent p-hacking Collaborate with Privacy, Legal, and Compliance to ensure methods meet regulatory and platform constraints Create operating rhythms: weekly measurement reviews, pre-mortems/post-mortems, and portfolio-level learning agendas Mentor and grow measurement scientists and engineers; set clear goals, career paths, and hiring plans Communicate outcomes to executives and customers in clear, actionable narratives (what changed, by how much, and what we will do next) Basic Qualifications Bachelors degree in a quantitative field - Computer Science, Operational Research, Statistics, Economics or related fields 10+ years of experience in leadership positions Preferred Qualifications Background in AI/ML techniques with applications to the Advertising domain. Proven experience designing and building scalable, reliable infrastructure for marketing technology platforms, with emphasis on data pipelines, eventing systems, and integration across adtech, CRM, and analytics ecosystems. A proven track record of delivering end-to-end solutions for high QPS systems, working with massive amounts of data. Proven experience designing and building scalable, reliable infrastructure for marketing technology platforms, with emphasis on data pipelines, eventing systems, and integration across adtech, CRM, and analytics ecosystems. Experience managing teams of 50+ individuals and first/second-line managers. Ability to lead by example and inspire the team to perform at a high level, and collaborate very well across different teams. Good understanding of large-scale engineering systems and some or all of big data technologies like Hadoop, Spark, distributed key-value stores, streaming processes, recommender systems, statistical methods, and experimental design. Highly motivated and able to work with ambiguous fast-changing problem landscapes, convert vague and ill-defined problems into well-defined problems, take initiative and encourage consensus building across partners. Strong leadership abilities in order to communicate and drive cross-functional efforts. Good relationship building and people skills. Publications at conferences and patents are highly desirable. Suggested Skills: * People Leadership * Ads experience * AI/ML experience You will Benefit from our Culture: We strongly believe in the well-being of our employees and their families. That is why we offer generous health and wellness programs and time away for employees of all levels. LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $231,000 to $378,000. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor. The total compensation package for this position may also include annual performance bonus, stock, benefits and/or other applicable incentive compensation plans. For more information, visit . Equal Opportunity Statement We seek candidates with a wide range of perspectives and backgrounds and we are proud to be an equal opportunity employer. LinkedIn considers qualified applicants without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other legally protected class. 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