Rishikesh Kulkarni
Senior Director, Machine Learning Engineering
π Lexington, MA | π§ rishi@kulkarni.science | π rukulkarni.com | π» github.com/rishi-kulkarni | πΌ linkedin.com/in/rishi-kulkarni
Professional Experience
Senior Director, Machine Learning Engineering
IntelyCare | 2025 β Present
- Leading a team of data scientists, software engineers, and data engineers to develop the one-stop shop for nurses seeking job opportunities.
Director, Data Science
IntelyCare | 2023 β 2025
- Led stakeholder buy-in and spearheaded the development of a dynamic Bayesian pricing model, significantly improving gross profit margins over previous static pricing strategies.
- Designed and implemented an AI-powered job matching system using custom embeddings and vector search in PostgreSQL, improving job application rates while meeting strict sub-100ms latency requirements.
- Led the development of an LLM-powered credentialing pipeline, automating the majority of document processing workflows without human intervention.
- Implemented robust monitoring systems using proper scoring rules and recurring validation tests to ensure continued model performance and detect potential drift over time.
- Led cross-functional initiatives to optimize database performance and cloud infrastructure, delivering projects ahead of schedule while achieving substantial cost reductions.
Data Science Manager
IntelyCare | 2022 β 2023
- Built and led a team of six data scientists and machine learning engineers, delivering data products, business intelligence tools, and automated inference solutions to support business operations.
- Implemented and deployed Bayesian decision theory-based workforce management solutions, reducing operational inefficiencies and enabling human resources to focus on complex cases while streamlining operations.
- Directed hierarchical forecasting efforts for supply and demand modeling, enhancing strategies across sales, marketing, and recruiting. Provided actionable financial forecasts to executive leadership, influencing company-wide strategy and operational decisions.
Senior Data Scientist
Tessella | 2021 β 2022
- Collaborated with clients in the pharmaceutical industry to enhance research and development processes through machine learning and advanced statistical modeling.
- Provided technical leadership on statistical methodologies and successfully established stakeholder and regulatory confidence in model validation frameworks.
- Implemented CI/CD pipelines utilizing Jenkins, Docker, and AWS Lambda for scalable deployment of data science solutions.
Computational Biology Postdoctoral Scholar
Stanford University | 2018 β 2021
- Conducted research in computational biology and molecular neuroscience, developing novel statistical methods for biological data analysis and experimental design optimization.
Education
PhD, Chemistry
University of California, Berkeley | 2013 β 2018BA, Biochemistry and Molecular Biology Boston University | 2009 β 2013
Open-Source Contributions
- bayesianbandits - A Python library for implementing Bayesian multi-armed bandit algorithms
- hierarch - A Python package for analyzing nested experimental designs
- Additional contributions to scipy, Apache Airflow, numba, and other open-source projects
Selected Publications
Analyzing nested experimental designsβA user-friendly resampling method to determine experimental significance.
PLoS Computational Biology. Kulkarni, et al.Voltage-sensitive rhodol with enhanced two-photon brightness.
PNAS. Kulkarni, et al.
Presentations
- “Strategic Pricing Using Cutting-Edge Methods and Data-Driven Solutions”
National Association of Business Economics TEC, Santa Clara, 2023
Last updated: May 2025