About the job
We are building large-scale, Azure-based intelligence platform that transforms complex data into high-quality and rich actionable insights to Microsoft Advertising stakeholders. The system integrates advanced machine learning models with emerging agentic capabilities powered by large language models (LLMs) to model recommendations, automate analysis, generate contextual summaries, and streamline workflows across organizational tools. As a Senior Applied Scientist, you will lead the development of these Machine Learning solutions leveraging SOTA technologies in GenAI to build predictive models for generating recommendations, detecting anomalies, generating automated insights with reasoning and ensuring the platform delivers accurate, actionable intelligence at scale.
Responsibilities
Lead the design and implementation of machine learning models for recommendations, anomaly detection, and actionable insights.Drive experimentation and validation of models. Mentor less experienced scientists and contribute to model governance and Responsible AI practices.Partner with engineering and BI teams to operationalize insights into dashboards and alerting systems.Advance feature adoption scoring and health check analytics through data-driven approaches.Engineer optimal prompts for various LLM calls, using chain-of-thought and other advanced techniques.Fine-tune SLMs to efficiently scale the solution to millions of advertisers.Generate account planning guidance, tailored talking points, pitch assets, follow ups using agentic orchestration.
Qualifications
Minimum
Bachelor'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 Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research)OR equivalent experience.
Preferred
Master'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 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.3+ years of hands-on experience developing machine learning or statistical models to solve real-world problems (in industry or academic projects), including building and validating algorithms such as regressions, classifiers, or clustering models.Proficiency in programming for data science (e.g. using Python or R for data analysis and modeling) and experience with data querying languages (e.g. SQL).Big Data & Distributed Computing: Hands-on experience with large-scale data processing using tools like Apache Spark or Azure Databricks for training and inference workflows.Advanced Analytics: Skilled in time-series analysis and anomaly detection techniques (e.g., ARIMA, isolation forests) applied to business contexts for actionable insights.LLMs & Domain Adaptation: Practical experience with prompt engineering, fine-tuning GPT-like models, and applying LLMs in domain-heavy areas (healthcare, agriculture, social sciences) while ensuring privacy and Responsible AI compliance.