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Machine Learning Consultant
Broad experience in businesses where machine learning is key to success. Provide consulting services and training on technical or general management problems and serve on BoD and advisory boards. Have founded several startups, initiated new businesses for large companies, done business evaluation, management consulting and corporate training. Technical expertise in machine learning and data science. Formal education in signal processing, finance and business. Excellent communications skills. Have designed machine learning systems for a number of applications including: algorithmic trading systems, content optimization for web site performance, bioinformatics applications. Deep understanding of modern machine learning algorithms including: regularized regression, boosted trees, support vector machines (SVM), neural nets, auto-encoders, deep belief networks (DBN), restricted Boltzmann machines (RBM), time series methods Kalman filtering, natural language processing, latent semantic analysis, topic modeling, big data techniques for machine learning on map reduce, recommender systems. Programming languages – Python, R, Tensor Flow, Theano, C++, Java, C, Unix, Fortran. Published work: Michael Bowles, "Machine Learning with Spark and Python: Essential Techniques for Predictive Analysis with Spark", 2nd Edition Wiley, Sept 2019 Mike Bowles and Ron Shigeta, "Statistical models for predicting liver toxicity from genomic data" to appear in Systems Biomedicine 1:3, 1-6 July/August/Sept 2013, Landes Bioscience. Jeremy Howard and Mike Bowles "The Two Most Important Algorithms in Predictive Modeling Today" O'Reilly Stata Conference 2012. ACM presentation using data mining to improve performance of a traditional trading system. The video and audio are on fora.tv: http://fora.tv/2009/05/13/Michael_Bowles_Neural_Nets_and_Rule-Based_Trading_Systems#post
UCLA Anderson School of Management
MBA, Finance and New Ventures
January 1, 1989 – January 1, 1991
Massachusetts Institute of Technology
ScD, Instrumentation
January 1, 1975 – January 1, 1980
Oklahoma State University
MSME, Estimation and Control
January 1, 1973 – January 1, 1975
Oklahoma State University
BSME, Mechanical Engineering
January 1, 1967 – January 1, 1972
Susquehanna International Group, LLP (SIG)
Machine Learning Consultant
April 1, 2019 – January 1, 2022
San Francisco Bay Area
Collaborative Drug Discovery - CDD VAULT
Machine Learning Consultant
August 1, 2018 – Present
Burlingame California
UC Berkeley School of Information
Machine Learning Lecturer
May 1, 2017 – January 1, 2019
Berkeley, California
SVXR, Inc.
Machine Learning Consultant
January 1, 2017 – April 1, 2019
Silicon Valley
Galvanize Inc
Machine Learning Professor
June 1, 2015 – February 1, 2017
San Francisco
IndieBio
Startup Mentor
January 1, 2015 – December 1, 2016
San Francisco
Mike Bowles Consulting
Principal Consultant
January 1, 2014 – Present
Redwood City, California, United States
SpAlgo
Chief Data Scientist
January 1, 2014 – August 1, 2015
Palo Alto, CA
Biomatica
Co-Founder
September 1, 2012 – January 1, 2015
Berkeley, CA
Hacker Dojo
Machine Learning Instructor
January 1, 2010 – January 1, 2015
Mike Bowles
Sole Proprietor
February 1, 2001 – January 1, 2014
iBeam Broadcasting
Founder and Chairman of Board
March 1, 1997 – October 1, 1999
Com21
Founding CEO
April 1, 1993 – December 1, 1996
Hughes Aircraft Company
Business Development
June 1, 1981 – April 1, 1993
MIT
C. Stark Draper Assistant Professor
November 1, 1979 – June 1, 1981
Cultural Fit Analysis
The candidate's background is heavily weighted towards entrepreneurship, consulting, and academia, with a strong focus on Machine Learning and Data Science. While this demonstrates innovation and a broad skill set, the direct alignment with a 'Big Data Engineer' role requires further validation. The experience is more aligned with data science, machine learning engineering, or leadership roles rather than core big data infrastructure engineering. The lack of specific big data technologies (e.g., Hadoop, Spark, Kafka, distributed databases) in the resume descriptions suggests a potential gap for a dedicated Big Data Engineer role, impacting cultural fit for a team focused on these specific technologies.
Soft Skills & Operational Fit
The candidate demonstrates strong leadership, entrepreneurial drive, and a history of building and scaling technical ventures. Their extensive teaching experience suggests strong communication and mentorship skills. The diverse industry exposure indicates adaptability and problem-solving capabilities. However, the resume descriptions are very high-level, making it difficult to assess specific operational fit or collaboration styles beyond leadership.