Jeremy Jacobson, Data Scientist and Developer in Atlanta, GA, United States
Jeremy Jacobson

Data Scientist and Developer in Atlanta, GA, United States

Member since January 20, 2022
Jeremy is a versatile data scientist with six years of experience that includes leading machine learning research projects and managing department teams. He has built data science and ML solutions used by faculty and external partners such as CareerBuilder and UPS. He was recognized by Google Education as a Google Cloud Faculty Expert and selected to the AWS Academy Cloud Council Faculty. Adept at picking up new skills quickly, Jeremy is committed to freelancing excellence.
Jeremy is now available for hire




Atlanta, GA, United States



Preferred Environment

Jupyter Notebook

The most amazing...

...thing I've worked is a GAN model that generates elliptic curves. It required new techniques for overcoming mode collapse.


  • Director of Technology

    2021 - PRESENT
    Emory University (Dep. of Quantitative Theory & Methods)
    • Advanced to Director of Technology after building a data science environment on AWS for two faculty and five researchers running large-scale linear job-matching models, ordered by CareerBuilder that wanted a transparent job matching model.
    • Mentored four student researchers and two faculty advisors in partnership with UPS on using the Google Cloud Platform for modeling and data engineering. The project led to UPS cutting costs and saving on labor.
    • Held three workshops and provided one-on-one training to department faculty on GPU computing. Performed the admin role on the department's GPU server.
    • Built a hosted notebook solution supporting ML workflows for our faculty.
    Technologies: Python, Bash, Jupyter, Neural Networks, NumPy, PyTorch, SQL, Pandas, Scikit-learn, R, Deep Neural Networks, Linux, Google Cloud, Machine Learning, Statistics, Data Science, Amazon SageMaker, Amazon Web Services (AWS), Algorithms, Artificial Intelligence (AI)
  • Lecturer

    2017 - PRESENT
    Emory University (Dep. of Quantitative Theory & Methods)
    • Recognized in 2020 by Google Education as one of thirty-four Google Cloud Faculty Experts. Selected in 2019 as one of eighteen AWS Academy Cloud Council Faculty, including members from MIT, Harvard, and Georgia Tech.
    • Supervised research, including an honors thesis that trains Generative Adversarial Networks (GAN) models on a novel dataset. Unlike typical GAN applications, this model generates mathematical objects.
    • Developed new techniques to overcome GAN problems such as mode collapse.
    • Designed highly lauded data science classes, QTM 250 and 350. Led over 500 students in building ML models on AWS and Google Cloud Platform (GCP) from conception through deployment.
    Technologies: PyTorch, TensorFlow, SQL, NumPy, Pandas, Scikit-learn, R, Google Cloud, Jupyter, Bash, Python, Neural Networks, Deep Neural Networks, Linux, Machine Learning, Statistics, Data Science, Amazon SageMaker, Amazon Web Services (AWS), Data Visualization, Data Modeling, Data Engineering, Algorithms, Time Series, Time Series Analysis, Financial Modeling, Amazon DynamoDB, Artificial Intelligence (AI)
  • ML Engineer (SageMaker) for Established AI Company

    2022 - 2022
    Toptal Client
    • Implemented a neural network-based time series model and trained it using custom Docker containers and AWS SageMaker training jobs. Decreased model building compute costs by a factor of 10 while enabling scalability to orders of magnitude more data.
    • Decomposed monolithic time series model code into processing, training, and deployment steps implemented in an AWS SageMaker Pipeline. Decreased lines of code by a factor of 10.
    • Mentored a team of three data scientists on using AWS SageMaker tools for MLOPs. Presented to directors in biweekly meetings.
    • Impressed the client (a large US rail company) with the project so much that they plan to use it as a building block for new work by their data science team.
    Technologies: Amazon SageMaker, Amazon Web Services (AWS), Deep Neural Networks, Data Science, Data Visualization, Data Modeling, Data Engineering, Algorithms, Time Series, Time Series Analysis, Supply Chain Optimization, Financial Modeling, Supply Chain, Supply Chain Management (SCM), Artificial Intelligence (AI)
  • Visiting Assistant Professor

    2013 - 2017
    Emory University (Dep. of Math. & Computer Science)
    • Developed and managed multiple research projects, two of which delivered publications in high-ranking journals.
    • Lectured as an invited speaker at scientific conferences in England, Germany, and the USA.
    • Investigated the use of TensorFlow and deep learning on a classification problem in algebraic geometry. Ported code to TensorFlow, benchmarked models and presented this work at the Meeting on Applied Algebraic Geometry, GaTech, 2018.
    • Led a team of three postdoctoral researchers in teaching Linear Algebra and Multivariable Calculus.
    • Managed curriculum development and evaluation and was recognized for teaching excellence, progressing to Lecturer.
    Technologies: TensorFlow, Deep Neural Networks, Linux, Mathematics, Jupyter, Bash, Python, Neural Networks, NumPy, Probability Theory, Markov Model, Data Science, Algorithms, Artificial Intelligence (AI)


  • Counting Real Roots Using Neural Networks

    In a .ipynb notebook file in the GitHub repository, I explain how to reproduce the results using Google Cloud, demonstrating how to use neural networks to count the number of real solutions to polynomial systems.

    For details, see the slides available at They are from an informal talk I gave as a part of the QTM chalk talk series.

  • Roman Urdu NLP

    A sentiment classifier that maximizes accuracy to provide a clear window into the social media conversations of the corporation's customer base. See the notebook (.ipynb file) in the included repo for details.

  • Model Creation and Evaluation for Sentiment Analysis

    Model creation and evaluation of machine learning models which automatically identify the sentiment of the conversations of a corporation's customer base on social media. We constructed a flexible Roman Urdu sentiment classifier suitable for multiclass or binary classification by composing existing ML models. Despite its simplicity, we have shown that its accuracy is competitive with state-of-the-art models from current research.


  • Tools

    Amazon SageMaker, Jupyter
  • Paradigms

    Data Science
  • Other

    Mathematics, Statistics, Machine Learning, Data Modeling, Algorithms, Time Series, Time Series Analysis, Artificial Intelligence (AI), Deep Neural Networks, Data Visualization, Data Engineering, Supply Chain Optimization, Financial Modeling, Supply Chain, Supply Chain Management (SCM)
  • Languages

    Python, Bash, SQL, R
  • Libraries/APIs

    NumPy, PyTorch, TensorFlow, Pandas, Scikit-learn
  • Platforms

    Amazon Web Services (AWS), Linux, Jupyter Notebook
  • Storage

    Google Cloud, Amazon DynamoDB


  • PhD in Mathematics and Computer Science
    2006 - 2012
    Louisiana State University - Baton Rouge, LA, USA
  • Bachelor's Degree in Mathematics and Computer Science
    2001 - 2005
    University of Wisconsin – Madison - Madison, WI, USA


  • Google Cloud Faculty Expert
    Google Cloud Education
  • AWS Certified Cloud Practitioner
    AUGUST 2019 - AUGUST 2022
    Amazon Web Services
  • AWS Academy Accredited Educator
    MARCH 2019 - PRESENT
    Amazon Web Services

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