Principal Applied Scientist - Sales Insights Platform at Microsoft

Redmond, Washington, United States -

Full Time

Start Date

Immediate

Expiry Date

20 Feb, 26

Salary

0.0

Posted On

22 Nov, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Data Science, Generative AI, NLP, Large Language Models, Predictive Analytics, Anomaly Detection, Clustering, Model Deployment, MLOps, Cloud Platforms, Python, PyTorch, TensorFlow, Statistical Analysis, Technical Leadership

Industry

Software Development

Description

Define and drive the modeling strategy for the Sales Insights platform, spanning classical machine learning (for analytics on structured data) and the use of generative AI (for insight summarization). You will set the direction on which problems to tackle with ML (e.g. anomaly detection, predictive modeling, clustering) and how to leverage LLMs to maximize user understanding and value. Architect end-to-end machine learning pipelines - oversee the design of data processing workflows, feature stores, model training/validation routines, and deployment mechanisms that can reliably produce daily insights for all customers. Ensure these pipelines are scalable, efficient, and maintainable, working closely with data engineering leaders on implementation. Lead the incorporation of LLM-based components for the platform's intelligent narrative generation. This includes guiding the development of prompt frameworks, fine-tuning strategies, and retrieval-augmented techniques so that the system can answer complex sales questions and explain insights in conversational language. Oversee cross-team initiatives and collaboration, coordinating with engineering, program management, and stakeholder teams. You will chair technical design reviews, balance priorities, and guarantee that the data science efforts align with product requirements and timelines. Mentor and develop the applied science team, providing technical guidance to other scientists and engineers. Champion best practices in experimentation, coding, and MLOps, and foster a culture of scientific excellence and continuous learning. Ensure robust evaluation and governance of all AI/ML solutions. You will establish metrics for success (accuracy, precision of alerts, coverage of insights), closely monitor model performance in production, and implement processes for periodic retraining, validation, and Responsible AI compliance (addressing bias, fairness, and transparency). Stay ahead of the curve by tracking emerging trends in AI, whether it's new algorithms in anomaly detection or breakthroughs in large language models, and assess their potential to enhance the platform. Drive the incubation of innovative ideas, experimentally verify their benefits, and incorporate promising approaches to keep the platform technologically ahead and highly effective. Bachelor's Degree in Computer Science, Statistics, Electrical/Computer Engineering, or related field AND 6+ years of related experience (e.g. machine learning, data science, AI product development); OR Master's Degree in a related field AND 4+ years of related experience; OR PhD in a related field AND 3+ years of related experience; 5+ years of experience with developing and deploying machine learning solutions in production, with proven ownership of complex projects end-to-end (from problem formulation and data acquisition to model deployment and monitoring). 3+ years of technical leadership experience in an applied science or data science team setting - this could include leading a team of scientists or acting as the key technical decision-maker on cross-discipline projects, with responsibility for delivering major features or systems. 3+ years of Extensive hands-on expertise in ML techniques for predictive analytics, pattern recognition, and optimization. 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. 5+ years experience creating publications (e.g., patents, libraries, peer-reviewed academic papers). 2+ years experience presenting at conferences or other events in the outside research/industry community as an invited speaker. 5+ years experience conducting research as part of a research program (in academic or industry settings). 3+ years experience developing and deploying live production systems, as part of a product team. 3+ years experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping. Extensive hands-on expertise in ML techniques for predictive analytics, pattern recognition, and optimization. You should be comfortable selecting and tuning algorithms for regression, classification, clustering, time-series forecasting, etc., and understand their trade-offs. Programming & Data Infrastructure: Proven coding skills in ML-focused languages (e.g., Python), experience with frameworks like PyTorch/TensorFlow, and familiarity with data pipelines and databases. LLMs & Domain Applications: Deep understanding of NLP and large language models, with hands-on experience in applying LLMs to domain-heavy contexts (e.g., healthcare, agriculture, social sciences) while ensuring privacy and Responsible AI compliance. Cloud & MLOps Expertise: Proven ability to build scalable ML pipelines on cloud platforms (Azure, etc.), implement automated training, monitoring, and governance for secure and compliant AI systems. Leadership & Impact: Track record of driving cross-organizational alignment, delivering innovative AI solutions, and contributing to research, patents, or community leadership.

Responsibilities

Define and drive the modeling strategy for the Sales Insights platform, overseeing the design of machine learning pipelines and ensuring they are scalable and efficient. Lead the incorporation of LLM-based components for intelligent narrative generation and mentor the applied science team.