Help & Supportcareers@gravityer.com
Senior Software Engineer at Google Deepmind
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
I like to build systems that do fascinating things with data. I'm well versed in machine learning (classification, regression, clustering) and have expertise in natural language processing. I like to take problems from raw, unstructured data to models that do amazing things. I also believe in using proper computer science algorithms, and I try to keep my big O complexities down.
The University of Edinburgh
MSc, Computer Science
January 1, 2010 – January 1, 2011
University of Minnesota
Bachelor of Science in Mathematics, Cryptography & Number Theory
January 1, 2004 – January 1, 2008
University of Minnesota
Bachelor of Science in Computer Science, Cryptography & Computer Security
January 1, 2004 – January 1, 2008
Anoka Ramsey Community College
PSEO Student (college classes while in high school)
January 1, 2001 – January 1, 2004
Software Engineer, Machine Learning
August 1, 2016 – Present
San Francisco Bay Area
Savvysherpa, Inc.
Research Scientist
November 1, 2015 – July 1, 2016
Greater Minneapolis-St. Paul Area
Contata Solutions
Research Scientist
November 1, 2014 – October 1, 2015
Minneapolis, Minnesota
Thomson Reuters
Research Engineer
July 1, 2012 – November 1, 2014
Eagan, Minnesota
Bloxx
Software Engineer
September 1, 2011 – June 1, 2012
Livingston, United Kingdom
Anoka Ramsey Community College
Computer Science Teaching Assistant
September 1, 2003 – May 1, 2004
Coon Rapids, Minnesota, USA
Anoka Ramsey Community College
Computer Science & Mathematics Tutor
September 1, 2002 – May 1, 2004
Coon Rapids, Minnesota, USA
Nystrom & Associates
IT Specialist
February 1, 2002 – September 1, 2010
New Brighton, Minnesota, USA
Simulated Annealing
September 1, 2015 – Present
A Simulated Annealing implementation with a scikit-learn style interface. Uses multiprocessed cross validation to estimate performance for a given parameter set and can work over grids or distributions over parameters.
Count Min Sketch
September 1, 2014 – Present
A Python implementation of the Count Min Sketch algorithm with some hacks for speed.
Insurinator
September 1, 2014 – Present
Calculation of best plans, break even points, natural language generation.
Streaming Gaussian Naive Bayes
August 1, 2014 – Present
Gaussian Naive Bayes trained in an online fashion using a numerically stable algorithm for estimating variance in a single pass.
LRU Cacher
July 1, 2014 – Present
A Least Recently Used Cache (LRU Cache) in Python
Cultural Fit Analysis
The candidate's career trajectory shows a consistent focus on machine learning and NLP, aligning well with an NLP Engineer role. The personal projects, such as 'Simulated Annealing', 'Count Min Sketch', and 'Streaming Gaussian Naive Bayes', demonstrate a proactive interest in fundamental algorithms and data structures, indicating intellectual curiosity and a drive for continuous learning. The diversity of roles from research scientist to software engineer suggests adaptability. However, the project descriptions lack specific technologies, making it harder to assess breadth of modern toolchain experience.
Soft Skills & Operational Fit
The candidate's experience as a Research Scientist and Software Engineer at multiple companies, including Google, suggests strong problem-solving abilities and a capacity for independent research and development. The descriptions of prototyping and taking ideas to full realization indicate a practical, results-oriented approach. However, without specific psychometric test results, a detailed assessment of work attitude, stress handling, and team collaboration is not possible.