Kelsey Macke

Denver, CO, USA

Fractional Data Scientist

Professionally, I am a data scientist with skills across a broad range of methodological and engineering competencies - data mining, automation, machine learning, visualization, cloud computing, algorithmic bias, and statistics. Personally, I am a creative mind driven by the opportunity to learn.


I love creative solution-building from messy problem sets and helping organizations get started using data in a more efficient, effective way. I am passionate about streamlining and scaling data pipelines, as well as creating robust equity-first algorithms. I am particularly excited about working with non-profits and small groups led by a mission for social good.

Kelsey Macke

Work

Experience

  • DataKind

    DataKind

    Senior Data Science Consultant

    Jan 2024 - Oct 2024

    • Proposed plan to scale and deploy student risk model, resulting in $8 million of funding from Google.org
    • Built modeling pipelines, evaluation systems, and responsible AI utilities in Azure Databricks, with a focus on student safety and supporting the most vulnerable populations
    • Mentored and collaborated with data scientists and leadership on methodological and tooling choices, as well as guided technical debt priorities and best practices on code quality
  • Panorama Education

    Panorama Education

    Senior Data Scientist

    Nov 2021 - Oct 2023

    • Led scoping and implementation of internal data platform, using PySpark, terraform, and Amazon Web Services (AWS), to create a scalable, practical, and secure system enabling data science, research, and product analytics work for the compan
    • Established equity-first hiring and leveling processes for both individual contributors and data science managers
  • Mathematica Policy Research

    Mathematica Policy Research

    Senior Data Scientist

    Apr 2019 - Oct 2021

    • Led grand-prize-winning team in Agency for Healthcare Research and Quality’s (AHRQ) data visualization challenge
    • Led modeling team selected as a finalist in the Centers for Medicare and Medicaid Services’ (CMS) AI Challenge
    • Developed deep learning methodology to predict hospitalizations and deaths with Google Cloud Platform (GCP), Luigi pipelines, adjustments for algorithmic bias, and an internal Python package
    • Developed a scraping pipeline to feed data into an AWS database, complete with unit, integration, and end-to-end testing, as well as continuous integration (CI) functionality
  • uAspire

    uAspire

    Manager of Research & Evaluation

    Jul 2018 - Apr 2019

    • Measured organization’s impact and predicted college enrollment of students utilizing randomized control trial data, propensity matching, and machine learning modeling
    • Automated data processing through custom-built tools, including an internal R package and user interface
    • Supervised analysts and steered team in expanding toolkit towards a broader use of R and Git
  • 84.51/dunnhumby (Kroger)

    84.51/dunnhumby (Kroger)

    Senior Data Scientist

    May 2013 - Apr 2018

    • Developed internal dynamic query generator to pull transactional data on over 55 million households and trained fellow analysts to use in their daily work
    • Leveraged NLP methodologies to generate and personalize digital content for America’s largest supermarket
    • Implemented variable reduction and unsupervised learning to cluster stores into pricing groups
  • Miami University

    Miami University

    Student

    Aug 2010 - May 2014

    • B.A. in Mathematics
    • B.S. in Quantitative Economics
    • M.A. in Applied Economics