Alexander Vidal

Alexander Vidal

Senior ML Researcher | ML for startups | PhD in Applied Mathematics & Statistics

Curriculum Vitae

Alexander Vidal

Senior ML Researcher | ML for startups | PhD in Applied Mathematics & Statistics

 alexanderrobertvidal@gmail.com

Education

Experience

  • Senior Machine Learning Researcher, Launch Potato, February 2025 – Present
    • Location: Remote / Durango, CO
    • Technical Leadership: Serve as the primary machine learning expert across all business verticals. Mentor fellow data scientists and lead strategic ML initiatives, particularly in recommendation systems.
    • Recommendation Systems: Identify opportunities for ranking and personalization (e.g., two-tower architectures). Lead technical implementation and contribute to engineering and MLOps.
    • Revenue Optimization: Develop utility-aware models to optimize revenue per click (RPC), bid adjustments, and downstream monetization strategies.
    • Learning with Bandits: Apply contextual and multi-armed bandits to optimize ad placement and recommendation performance under uncertainty.
    • Statistical Inference: Conduct A/B tests and use statistical methods to quantify the effectiveness of ML-driven interventions.
  • Senior Data Scientist, On The Barrelhead / NerdWallet, October 2021 – August 2024
    • Location: Remote / Durango, CO
    • Technical Leadership: Led the data science team for Credit Cards and Lending, mentoring a direct report and driving strategic machine learning initiatives.
    • Startup Transition: Spearheaded the integration of On The Barrelhead’s data science operations into NerdWallet post-acquisition, enhancing ML capabilities across product pages.
    • Revenue Growth: Designed and deployed recommendation algorithms for product roundups, increasing average revenue per session by 15%.
    • Synthetic Data Generation: Leveraged generative modeling to augment class-imbalanced datasets, improving ML performance in key applications.
    • User Engagement Optimization: Developed predictive models for personalized product recommendations, increasing click-through rates by 10%.
    • Risk Management: Applied portfolio optimization techniques to determine the ideal product mix, balancing risk and user value.
  • Chief Data Scientist, Rigorous Machine Learning Solutions, LLC, October 2022 - Present
    • Consulted on predictive data modeling projects for Regenexx, Verra, and On The Barrelhead (before starting full-time as a senior data scientist).
  • National Science Foundation (NSF) Intern, USGS, June 2021-August 2021
    • Worked with the USGS hyperspectral team to collect hyperspectral data for more accurate predictive analysis.
  • Graduate Research Assistant, CASERM, 2019 - 2021
    • Collected two different types of mineralogical data and applied image recognition techniques in order to reconcile the two datasets.
    • A preprocessing step was applied that uses convolutional neural networks to “mask” the data that is not useful.
    • A stochastic autoencoder (SAE) is used to ‘mix’ the data used from different sources a latent space.
    • Neural network is used to allow for prediction of one dataset given the other.
  • Data Science Intern, Lumen Technologies (formerly CenturyLink), June 2019 - August 2019
    • Working Group: Finance
    • Classified pdf documents using deep learning and natural language processing (NLP).
  • Teaching Assistant, Colorado School of Mines, 2018-2021
    • Classes: MATH534/535: Mathematical Statistics, MATH537: Multivariate Analysis, MATH536: Advanced Statistical Modeling, MATH225: Differential Equations.

Computer Skills

  • Programming languages: Python, R, Matlab, Bash/Shell script, LaTeX, PostgreSQL, MSSQL
  • Packages: Numpy, Scipy, Pandas, scikit-learn, Pytorch, Keras, Tensorflow, CVX, CVXPy
  • Operating Systems: MacOS, Linux, Windows
  • Other: Git

Software Publications and Contributions

Peer-Reviewed Publications

Manuscripts in Preparation and Preprints

Conference Contributions & Talks