# Principal Applied Scientist **Company**: Microsoft **Location**: Sunnyvale **Work arrangement**: hybrid **Experience**: senior **Job type**: full-time **Salary**: $142,800 - $274,800 per year **Category**: Engineering **Industry**: Technology **Ticker**: MSFT **Wikidata**: https://www.wikidata.org/wiki/Q2283 **Apply**: https://microsoft.ai/job/principal-applied-scientist-55/?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_b3831ee5-204 ## Description Our Signals Modeling team builds the intelligence that powers how the advertising marketplace understands user behaviour, measures impact and optimises outcomes from initial impressions through downstream conversions and long-term advertiser value. We develop large-scale learning systems that infer intent and causal effects from incomplete and noisy feedback, enabling principled decision-making across ranking, bidding, pricing, and budget allocation. Our work sits at the foundation of marketplace optimisation, where accurate attribution and measurement directly influence billions in advertising spend. As a Principal Applied Scientist, you will help define the future of data-driven attribution and causal measurement, shaping the methodologies that determine how value is estimated and optimised across the ecosystem. You will partner across research, engineering, and product leadership to introduce advanced inference techniques into production systems operating at massive scale. This is a high-ownership role focused on solving structurally hard problems where ground truth is limited, experimentation is non-trivial, and scientific rigor is essential to unlocking durable marketplace advantage. Responsibilities: Define and drive the scientific and technical strategy for data-driven attribution (DDA) and causal measurement across advertising systems. Establish methodologies for incrementality estimation, counterfactual learning, delayed-feedback modelling, and bias correction in environments with partial observability. Lead the design and production adoption of attribution and causal inference frameworks that improve bidding, ranking, optimisation, and advertiser ROI at web scale. Set evaluation standards that distinguish correlation from causation and elevate experimental rigor across teams. Identify capability gaps and introduce advanced research, tools, or modelling approaches to strengthen measurement foundations. Operate across organisational boundaries to align research, engineering, product, and business leaders on measurement strategy. Serve as a subject-matter expert and technical advisor on attribution and causal inference. Mentor scientists and influence technical direction to raise the organisation's scientific bar. Qualifications: Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. Preferred Qualifications: Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 9+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. Demonstrated track record of setting technical direction for large-scale machine learning or statistical systems that delivered measurable business impact. Deep expertise in causal inference, data-driven attribution, treatment effect estimation, counterfactual learning, or experimental design , applied in production environments. Experience leading ambiguous, high-impact initiatives where ground truth is limited and methodological rigor is critical. Proven ability to influence strategy and drive adoption of new measurement or modelling approaches beyond an immediate team. Significant experience developing and deploying production ML systems across multiple stages of the product lifecycle. Solid scientific judgment with a history of selecting appropriate methodologies under real-world constraints. Exceptional communication skills with the ability to translate complex technical concepts into guidance for senior technical and business leaders. Recognised expertise in attribution, incrementality, marketplace experimentation, or causal ML. Track record of driving multi-year research or modelling agendas that materially improved product outcomes. Experience defining measurement strategy for advertising platforms, marketplaces, or large-scale recommendation systems. Publications, patents, or widely adopted internal methodologies in causal inference, experimentation, econometrics, or applied machine learning. History of mentoring senior scientists and elevating organisational scientific capability. Experience influencing director- or VP-level technical strategy. ## Skills ### Required - Statistics - Econometrics - Computer Science - Electrical or Computer Engineering - Predictive Analytics - Research ### Nice to have - Causal Inference - Data-Driven Attribution - Treatment Effect Estimation - Counterfactual Learning - Experimental Design --- Source: [Apply at microsoft.ai](https://microsoft.ai/job/principal-applied-scientist-55/?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)