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Lead Data Scientist at Target
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
University of Minnesota
Doctor of Philosophy (PhD), Astrophysics
January 1, 2009 – January 1, 2014
University of Washington
Bachelor's degree, Physics and Astrophysics
January 1, 2003 – January 1, 2008
Target
Lead Data Scientist
May 1, 2024 – Present
Target
Senior AI Scientist
April 1, 2021 – April 1, 2024
Calabrio, Inc.
Senior Machine Learning Engineer
January 1, 2020 – April 1, 2021
Calabrio, Inc.
Machine Learning Engineer
October 1, 2015 – January 1, 2020
Contata Solutions
Machine Learning Applications Developer
February 1, 2015 – October 1, 2015
Greater Minneapolis-St. Paul Area
Boom Lab
Consultant/Data Scientist
January 1, 2014 – February 1, 2015
Greater Minneapolis-St. Paul Area
University of Minnesota
Researcher
January 1, 2013 – September 1, 2014
University of Minnesota
Lead Lecturer
May 1, 2012 – August 1, 2012
University of Minnesota
Researcher
September 1, 2011 – September 1, 2013
University of Minnesota
Researcher
July 1, 2009 – August 1, 2011
University of Washington
Researcher
September 1, 2008 – September 1, 2009
Simulated Annealing for Hyperparameter Optimization in Python
September 1, 2015 – Present
This module provides a hyperparameter optimization using simulated annealing. It has a SciKit-Learn-style API and uses multiprocessing for the fitting and scoring of the cross validation folds. The benefit of using Simulated Annealing over an exhaustive grid search is that Simulated Annealing is a heuristic search algorithm that is immune to getting stuck in local minima or maxima.
Search with ML Certificate
Uplimit
June 24, 2026 – Present
Cultural Fit Analysis
The candidate has a strong academic background and a clear career progression in data science and machine learning, including roles at large enterprises like Target. Their personal project on Simulated Annealing for hyperparameter optimization demonstrates initiative and a continuous learning mindset. The diverse experience from academic research to industry roles, including consulting, suggests adaptability. However, the lack of explicit team collaboration or leadership project descriptions beyond 'Lead Data Scientist' role title makes it difficult to fully assess cultural fit for a highly collaborative lead role without further information.
Soft Skills & Operational Fit
The candidate's experience as a Lead Lecturer and Researcher at the University of Minnesota suggests strong communication, leadership, and project coordination skills. Their work on rapid prototyping teams at Calabrio indicates an ability to quickly test and implement new research, which aligns with agile operational models. The description of identifying trends and building production-ready software points to a practical, results-oriented approach.