Rishikesh Ksheersagar

Rishikesh Ksheersagar

Data Scientist at LatentView Analytics (CW at HP Inc.)
Masters in Data Science at University of Michigan, 2025
NLP Researcher at LIT Lab @ UMich
Previously - Data Science Manager at Mu Sigma Inc.

Data Scientist

Data Scientist — HP Personal Systems Analytics

Employer: LatentView Analytics Client: HP Inc. Duration: 2025–Present

At LatentView, I work as a Data Scientist supporting HP’s Personal Systems analytics and AI initiatives. My work focuses on building machine learning and Generative AI systems that analyze large-scale device telemetry and customer support data to improve product reliability, identify recurring device issues, and enable faster operational decision-making.

Key Contributions

  • Developing LLM-based systems for support analytics to process and analyze large volumes of customer support case notes, including automated summarization and classification of technical issues across device families.

  • Building large-scale semantic embedding pipelines using Sentence Transformers on Azure Databricks to transform hundreds of thousands of support records into vector representations for similarity search, clustering, and trend analysis.

  • Designing machine learning pipelines for categorizing support cases into structured issue taxonomies (multi-level labels), enabling scalable monitoring of recurring product issues and operational bottlenecks.

  • Analyzing device telemetry and system alerts (boot performance, thermal signals, battery health, and system events) to identify reliability trends and support proactive monitoring of device performance across large device fleets.

  • Developing automated analytics and reporting pipelines that convert raw operational data into structured insights and monitoring dashboards for engineering and product teams.

  • Implementing scalable data processing workflows using Python, PySpark, and SQL on Azure Databricks to support machine learning pipelines and large-scale analytics workloads.

Technologies

Python • PySpark • SQL • Azure Databricks • Machine Learning • NLP • LLM workflows • Vector embeddings • Data pipelines