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Artificial Intelligence
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
A decade of AI experience before it was cool. I started working in Bayesian models in 2016 with a Google Brain team that spun off to a startup, then transitioned into neural networks. After spending 4 years working on LLMs for NLP for top ML labs in industry I transitioned into cancer detection and pragmatic reinforcement learning for education technology. Now I'm working on computer vision and automation, specifically self driving cars and mining vehicles.
University of Chicago
Honors Bachelor of Science, Mathematics
N/A – Present
University of Michigan
Doctor of Philosophy (PhD), Mathematics
N/A – Present
Helm.ai
Research Engineer
January 1, 2026 – Present
New York, NY · Remote
Kiddom
AI Researcher
February 1, 2024 – January 1, 2026
New York, New York, United States · Remote
Ezra
AI Researcher
March 1, 2023 – November 1, 2023
New York, New York, United States · On-site
High Slope
Advisor
December 1, 2022 – Present
Kepler
Founding Machine Learning Scientist
April 1, 2022 – November 1, 2022
New York, New York, United States
ASAPP
Research Scientist
February 1, 2018 – March 1, 2022
Greater New York City Area
Kiddom
Machine Learning at Kiddom
June 1, 2016 – February 1, 2018
San Francisco
Scaled Inference
Inference Scientist Intern
January 1, 2016 – April 1, 2016
Palo Alto, California
Harvard University
Adjunct
September 1, 2013 – September 1, 2015
Cambridge, Massachusetts
Cultural Fit Analysis
The candidate has worked in diverse environments, from startups (Kepler, Ezra, Kiddom) to larger research-focused companies (ASAPP, Helm.ai), and even academia (Harvard). This breadth suggests adaptability. The focus on AI/ML research and product development aligns well with innovative, fast-paced cultures. However, the target role is 'Data Analyst', which is a significant shift from their extensive 'AI Researcher' and 'Machine Learning Scientist' roles. While their analytical skills are strong, the direct alignment with typical data analyst responsibilities (e.g., dashboarding, specific business intelligence tools, A/B testing design) is not explicitly detailed, which could impact cultural fit for a pure data analyst role.
Soft Skills & Operational Fit
The candidate's experience descriptions highlight collaboration (e.g., 'Collaborated on everything', 'Selected evaluation sets, training sets with colleagues'), leadership (e.g., 'Started and ran the research seminar', 'Managed one contracting scientist'), and a results-oriented approach (e.g., 'delivered 4 customer releases', 'Revenue generated'). These indicate strong operational fit and soft skills for a senior role.