David Lisuk
Data AnalystFounding Engineer at Distyl AI
Distyl AI
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Founding Engineer at Distyl AI
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Experienced Machine Learning Engineer with emphasis on product development and productionalization of models. Knowledgeable about machine learning, spark, full stack development, and micro service architectures.
University of California, San Diego
Master's Degree, Computer Science
January 1, 2013 – January 1, 2014
University of California, San Diego - Extension
Non Degree, Computer Science
January 1, 2011 – January 1, 2013
UC San Diego
BS, Bioengineering, Math (Minor)
January 1, 2006 – January 1, 2010
Galt HIgh
High School/Secondary Diplomas and Certificates
January 1, 2002 – January 1, 2006
Distyl AI
Director of Engineering
May 1, 2023 – Present
Palantir Technologies
Head of AI/ML Engineering
December 1, 2021 – March 1, 2023
Palantir Technologies
Lead ML Product Engineer
November 1, 2018 – November 1, 2021
Palantir Technologies
Forward Deployed Engineer - Machine Learning
March 1, 2015 – October 1, 2018
Palantir Technologies
Machine Learning Software Engineer - Intern
June 1, 2014 – September 1, 2014
Palo Alto, CA
UC San Diego
Teaching Assistant
August 1, 2013 – December 1, 2014
CoreLogic
Statistical Analyst
August 1, 2010 – January 1, 2014
Carlsbad, CA
Salk Institute for Biological Studies
Matlab Developer/Research Assistant
September 1, 2009 – September 1, 2013
UC San Diego - CRBS
Student Computer Analyst
March 1, 2008 – July 1, 2010
UC San Diego - Department of Mechanical & Aerospace Engineering
C Programming Tutor
January 1, 2008 – March 1, 2008
iDcDashboard -- Interactive Plotting for iPython Notebooks
December 1, 2014 – Present
iDcDashboard introduces a system to easily insert dc.js interactive plots into iPython notebooks. My MS research project.
MapFold: A Pipelined Distributed Data Processing Framework
May 1, 2014 – Present
MapFold is a distributed processing framework for parallelizing stateful iterative algorithms (such as stochastic gradient descent) across many systems. By parallelizing disk IO/learning many models simultaneously the system has significant performance gains over non distributed implimentations.
Cultural Fit Analysis
The candidate's career trajectory shows a strong focus on Machine Learning and Data Engineering, with significant experience at Palantir Technologies. The personal projects (iDcDashboard, MapFold) demonstrate initiative and a deep interest in data visualization and distributed systems. While the experience is heavily skewed towards ML Engineering and leadership, the target role is 'Data Analyst'. This represents a potential mismatch in scope, as the candidate's experience far exceeds a typical Data Analyst role, potentially indicating overqualification or a desire for a different career path. The breadth of skills is strong within ML/Data Engineering, but the direct alignment with a pure 'Data Analyst' role, which often focuses more on reporting, dashboarding, and ad-hoc analysis rather than ML system development, is not perfectly aligned. The candidate's background suggests a more senior, strategic, or architect-level role in data science or ML engineering would be a better fit.
Soft Skills & Operational Fit
The candidate demonstrates strong leadership and mentoring capabilities through roles like Head of AI/ML Engineering and organizing reading groups. Experience as a Teaching Assistant and Tutor also highlights communication and instructional skills. The ability to instigate and lead transitions (e.g., Perl/Java to Scala) indicates proactive problem-solving and operational improvement drive. However, the provided data does not include specific psychometric test results to fully assess work attitude, stress handling, or team collaboration beyond what can be inferred from leadership roles.