Rishikesh Kulkarni

Senior Director, Machine Learning Engineering

📍 Lexington, MA | 📧 rishi@kulkarni.science | 🌐 rukulkarni.com | 💻 github.com/rishi-kulkarni | 💼 linkedin.com/in/rishi-kulkarni

Professional Experience

Senior Director, Machine Learning Engineering

IntelyCare | 2025 – 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 – 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 – 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 – 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 – 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 – 2018

• BA, Biochemistry and Molecular Biology Boston University | 2009 – 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 designs—A 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