Rishikesh Kulkarni

Vice President, Engineering and Machine Learning/Data Science

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

Professional Experience

Vice President, Engineering and Machine Learning/Data Science

IntelyCare | 2026 – Present

• Leading the engineering, machine learning, and data science organizations.

Senior Director, Machine Learning Engineering

IntelyCare | 2025 – 2026

• Led 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

Publications

Peer-Reviewed Articles

† Co-corresponding author.

• Day EC, Chittari SS, Cunha KC, Zhao RJ, Dodds JN, Davis DC, Baker ES, Berlow RB, Shea J-E, Kulkarni RU, Knight AS. A high-throughput workflow to analyze sequence-conformation relationships and explore hydrophobic patterning in disordered peptoids. Chem 10, 3444–3458 (2024).

• Delaveris CS, Wang CL, Riley NM, Li S, Kulkarni RU†, Bertozzi CR†. Microglia mediate contact-independent neuronal network remodeling via secreted neuraminidase-3 associated with extracellular vesicles. ACS Central Science 9, 2108–2114 (2023).

• Kulkarni RU†, Wang CL, Bertozzi CR†. Analyzing nested experimental designs—a user-friendly resampling method to determine experimental significance. PLoS Computational Biology 18, e1010061 (2022).

• Franke JM, Raliski BK, Boggess SC, Natesan DV, Koretsky ET, Zhang P, Kulkarni RU, Deal PE, Miller EW. BODIPY fluorophores for membrane potential imaging. Journal of the American Chemical Society 141, 12824–12831 (2019).

• Adil MM, Rao AT, Ramadoss GN, Chernavsky NE, Kulkarni RU, Miller EW, Kumar S, Schaffer DV. Dopaminergic neurons transplanted using cell-instructive biomaterials alleviate parkinsonism in rodents. Advanced Functional Materials 28, 1804144 (2018).

• Kulkarni RU, Vandenberghe M, Thunemann M, James F, Andreassen OA, Djurovic S, Devor A, Miller EW. In vivo two-photon voltage imaging with sulfonated rhodamine dyes. ACS Central Science 4, 1371–1378 (2018).

• Adil MM, Gaj T, Rao AT, Kulkarni RU, Fuentes CM, Ramadoss GN, Ekman FK, Miller EW, Schaffer DV. hPSC-derived striatal cells generated using a scalable 3D hydrogel promote recovery in a Huntington disease mouse model. Stem Cell Reports 10, 1481–1491 (2018).

• Kulkarni RU, Miller EW. Voltage imaging: pitfalls and potential. Biochemistry 56, 5171–5177 (2017).

• Adil MM, Vazin T, Ananthanarayanan B, Rodrigues GMC, Rao AT, Kulkarni RU, Miller EW, Kumar S, Schaffer DV. Engineered hydrogels increase the post-transplantation survival of encapsulated hESC-derived midbrain dopaminergic neurons. Biomaterials 136, 1–11 (2017).

• Rodrigues GMC, Gaj T, Adil MM, Wahba J, Rao AT, Lorbeer FK, Kulkarni RU, Diogo MM, Cabral JMS, Miller EW, Hockemeyer D, Schaffer DV. Defined and scalable differentiation of human oligodendrocyte precursors from pluripotent stem cells in a 3D culture system. Stem Cell Reports 8, 1770–1783 (2017).

• Kulkarni RU, Kramer DJ, Pourmandi N, Karbasi K, Bateup HS, Miller EW. Voltage-sensitive rhodol with enhanced two-photon brightness. Proceedings of the National Academy of Sciences 114, 2813–2818 (2017).

• Knight AS, Kulkarni RU, Zhou EY, Franke JM, Miller EW, Francis MB. A modular platform to develop peptoid-based selective fluorescent metal sensors. Chemical Communications 53, 3477–3480 (2017).

• Kulkarni RU, Yin H, Pourmandi N, James F, Adil MM, Schaffer DV, Wang Y, Miller EW. A rationally designed, general strategy for membrane orientation of photoinduced electron transfer-based voltage-sensitive dyes. ACS Chemical Biology 12, 407–413 (2017).

• Adil MM, Rodrigues GMC, Kulkarni RU, Rao AT, Chernavsky NE, Miller EW, Schaffer DV. Efficient generation of hPSC-derived midbrain dopaminergic neurons in a fully defined, scalable, 3D biomaterial platform. Scientific Reports 7, 40573 (2017).

• Deal PE, Kulkarni RU, Al-Abdullatif SH, Miller EW. Isomerically pure tetramethylrhodamine voltage reporters. Journal of the American Chemical Society 138, 9085–9088 (2016).

Book Chapters

• Deal PE, Grenier V, Kulkarni RU, Liu P, Walker AS, Miller EW. Making life visible: fluorescent indicators to probe membrane potential. In Make Life Visible, 89–104. Springer Singapore (2019).

Preprints

• Elder BM, Kulkarni RU, Knight AS. Chemically informed representations enable prediction of amphiphilic copolymer solution behavior. ChemRxiv (2026).

• Kulkarni RU, Gest AMM, Lam CK, Raliski BK, James F, Adil MM, Schaffer DV, Wang Y, Miller EW. Computationally assisted design of high signal-to-noise photoinduced electron transfer-based voltage-sensitive dyes. ChemRxiv (2020).

• Kulkarni RU, Wang CL, Bertozzi CR. Subthreshold voltage analysis demonstrates neuronal cell-surface sialic acids modulate excitability and network integration. bioRxiv (2020).

Patents

• Miller EW, Kulkarni RU. Photoinduced electron transfer voltage-sensitive compounds. US Patent 10,370,351 (2019).

Presentations

• “Strategic Pricing Using Cutting-Edge Methods and Data-Driven Solutions”
  National Association of Business Economics TEC, Santa Clara, 2023

Last updated: August 2026