Resume

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Tom Finzell — Resume · Updated April 2026
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Data Scientist and Astrophysicist with expertise in machine learning, data engineering, and scientific computing. Experienced building end-to-end data pipelines and ML systems on AWS. Proven leader of cross-functional technical teams, with a track record of delivering reproducible, data-driven solutions to complex real-world problems.

Technical Skills
Infrastructure AWS (Lambda, Step Functions, DynamoDB, S3, CloudFront, CDK, Fargate, CloudWatch, SNS), CI/CD, Infrastructure as Code
Data Engineering ETL Pipelines, Data Validation, Schema Design, Contract-First Architecture (Pydantic), Data Wrangling
ML / Analysis Python, SQL, R, C++, Pandas, NumPy, scikit-learn, TensorFlow/Keras, Jupyter, Regression, Neural Networks, Cluster Analysis, Transformers, EDA
Scientific Time-Series Analysis, Signal Processing, Predictive Modeling, Experimental Design, Uncertainty Quantification, Spectroscopy/Photometry
Professional Experience
Independent Data Platform Project Feb 2026 – Present
Open Nova Catalog
  • Designed and built a serverless data platform that ingests, validates, and serves astronomical observation data from public archives — solo project covering architecture, infrastructure, backend, frontend, and documentation.
  • Architected full AWS infrastructure using CDK (Python): 17 Lambda functions, 7 Step Functions workflows, Fargate-based artifact generation, DynamoDB (single-table design), S3/CloudFront delivery.
  • Built a contract-first Python backend with Pydantic models at every service boundary, mypy strict enforcement, and end-to-end smoke tests against live AWS infrastructure.
  • Developed ingestion pipeline handling heterogeneous data from multiple public archives — identity resolution via coordinate-based deduplication, profile-driven FITS validation, SHA-256 fingerprinting, and explicit quarantine semantics for irreconcilable conflicts.
  • Implemented LTTB and density-preserving log downsampling algorithms for scientific time-series visualization; designed multi-band photometry offset algorithm using spline fitting and union-find clustering.
  • Built React/Next.js frontend with interactive Plotly.js scientific visualizations (spectra waterfall plots, multi-regime light curves) and a semantic design token system.
  • Wrote 30+ architectural decision records before implementation — covering identity resolution, data validation, quarantine semantics, downsampling algorithms, and immutable release model.
  • Designed an immutable artifact release model: all data products written to a new S3 prefix before an atomic pointer update makes them visible. Rollback is a single JSON write.
Assistant Professor, Computer Science (Visiting) Sep 2023 – Sep 2025
Carleton College
  • Led 30 applied machine learning projects on real-world datasets, guiding teams through problem formulation, feature engineering, model validation, and reproducible delivery.
  • Designed and led a project-based machine learning course; 10+ student teams per term built end-to-end ML pipelines (regression, deep learning, cluster analysis, transformers, EDA) on real datasets.
  • Provided technical mentorship on scalable architecture, testing strategies, and modular software design.
Assistant Professor, CMSE (Visiting) Sep 2020 – Aug 2023
Michigan State University
  • Managed a cross-functional team of 5+ faculty and 20+ teaching assistants delivering a multi-section introductory physics sequence.
  • Directed the iterative improvement of curriculum using performance data.
  • Facilitated weekly coordination meetings with stakeholders to align on goals and progress.
Assistant Professor, Physics & Astronomy (Visiting) Sep 2019 – Aug 2020
Macalester College
  • Created a Computational Physics course from scratch: curriculum design, materials development, and instruction in numerical methods, simulation, and scientific computing (Python).
Postdoctoral Researcher, Physics Jun 2017 – Aug 2019
University of Michigan
  • Founded and led a cross-functional program involving faculty, assistants, and stakeholders; secured funding and delivered measurable outcomes.
  • Redesigned data-driven workflows impacting courses serving ~1,200 users annually.
  • Supervised three undergraduate researchers through a project resulting in a peer-reviewed poster at the 2019 AAPT national meeting.
Graduate Research Assistant Sep 2011 – May 2017
Michigan State University
  • Constructed the original Open Nova Catalog dataset — data wrangling and integration across 10+ heterogeneous instruments with inconsistent formats, units, and metadata.
  • Developed 2D Fourier image reconstruction pipelines for interferometric radio data.
  • Built computational models of radio emission from expanding plasma shells.
  • Analyzed multi-epoch time-series brightness measurements to characterize transient astrophysical events.
Education
Ph.D. in Astrophysics — Michigan State University
2017
B.S. in Astronomy, B.S. in Physics — University of Wisconsin–Madison
2011
Minor in Computer Science — University of Wisconsin–Madison
AWS Cloud Practitioner AWS Solutions Architect
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