Evan Curtin

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Evan Curtin

Principal Applied Scientist shipping LLM systems and evaluation in active litigation workflows

Brooklyn, NY | evanmcurtin@gmail.com | github.com/ecurtin2

Skills

ML: Applied LLMs & Agents, NLP, Evaluation (LLM + offline + production), Information retrieval (RAG), Active learning

Core: Python, SQL, NumPy/Pandas

Systems: Docker, Kubernetes, Spark, GitOps CI/CD, Terraform, Databricks, Prefect, AWS/Azure, Polars, Elasticsearch, C++/Fortran, Rust, Julia, DataFusion

Experience

Principal Applied Scientist - Relativity

2025 – Present, Brooklyn, NY

Previously: Staff (2024–2025) • Lead (2022–2024)

  • Shipped LLM-based document classification for eDiscovery (Relativity aiR for Review); exceeded contract-review performance (industry standard) and ran on dozens of millions of documents in active litigations.
  • Built privacy-preserving evaluation pipelines and reliability metrics for LLM systems; enabled hundreds of experiments on sensitive legal data and partnered directly with AmLaw 100 firms (incl. partners) on active-litigation experimentation, enablement, and sales conversations.
  • Built LLM workflows for damages assessment in high-value litigation; cut time per example from ~1 day to <2 hours and saved an estimated ~600,000 hours across a large law firm.

Data Engineer - Coalition, Inc

2021 – 2021, Chicago, IL (Remote)

  • Built CI for fast-changing insurance pricing models during hypergrowth; improved algorithm performance ~6× while reducing deployment risk.
  • Scaled pricing systems with Dask and SageMaker; integrated real-time vulnerability data and deployed with Terraform.

Principal Machine Learning Scientist - Capital One

2019 – 2021, Chicago, IL

  • Built daily-refit fraud detection pipelines processing >1TB/day; prevented ~$14M/year in fraud losses and deployed daily with >95% success in a regulated environment.
  • Designed readiness checks (calibration + out-of-band validation) and GitOps CI/CD for fast iteration and safe rollback.

Data Science Engineer - Broadspire (Crawford & Company)

2018 – 2019, Chicago, IL

  • Built XGBoost insurance risk models for adjuster triage; cut training time 90% with Dask and deployed via low-maintenance AWS Lambda services.

Education

PhD in Chemistry (ABD - All but Dissertation), University of Illinois, Urbana-Champaign

Focus: Stability of Hartree-Fock Equations in symmetry-breaking solids

Technologies: C++, SLEPc, Blue Waters supercomputer

MS and BS in Chemistry, Drexel University

Thesis: Low Dimensional Models for Predicting Nanomaterial Properties. Physics Minor.

Publications

  1. E. Yang, E. Curtin, et al., "Beyond the bar: generative AI as a transformative component in legal document review," 2024 IEEE Int. Conf. Big Data, 2024.
  2. G. Bazargan, E. Curtin, K. Sohlberg, "Comparing statistical predictions of quantum particle transit times in molecular systems to experimental measurements," J. Theor. Comput. Chemistry, vol. 18, no. 8, p. 1950039, 2019.
  3. E. Curtin, G. Bazargan, K. Sohlberg, "Quantifying electron transit in donor-bridge-acceptor systems using probabilistic confidence," J. Theor. Comput. Chemistry, vol. 17, no. 7, p. 1850046, 2018.
  4. E. M. Curtin, K. Sohlberg, "A reduced dimensionality model of torsional vibrations in star molecules," Physica E: Low-Dimensional Syst. Nanostructures, vol. 77, pp. 131-137, 2016.
  5. J.-C. Bradley, A. Lang, A. Williams, E. Curtin, "ONS open melting point collection," Nature Precedings, 2011.

Projects

  • Quantized: An easy to use Quantum Mechanics Library in Python
  • Cookiecutter-Python: A project template for python projects with CI and Codespaces support
  • nbsanity: Jupyter notebook linter written in Rust
  • mkdocs-apidoc: Plugin for using mkdocs with autogenerated API documentation
  • Hartree-Fock Stability: Find if a HF solution is unstable with high performance linear algebra