Emanuel Azcona
ML EngineerAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Lead Data Scientist @ Nike | Product Creation & Merchandising DS
I'm a Lead Data Scientist focused on delivering end‑to‑end data science solutions to support Nike’s product creation ecosystem. My work integrates generative AI, structured extraction pipelines, multimodal evaluation frameworks, and scalable systems to generate high‑quality product metadata and insights for designers, merchants, and cross‑functional partners. Most recently, I have lead initiatives in VLM evaluation, product metadata generation through gen-AI, and assisting with 3D asset creation workflows, ensuring DS solutions produce outputs that are accurate, trustworthy, perceivable, and aligned with business needs. My work helps Nike capitalize on cutting-edge AI responsibly and effectively to deliver the right products to the right consumers at the right time.
Northwestern University
Master of Science - MS, Electrical Engineering
September 1, 2017 – June 1, 2019
Northwestern University
Doctor of Philosophy (PhD), Electrical Engineering
September 1, 2017 – June 1, 2022
NYU Tandon School of Engineering
Bachelor of Science, Electrical Engineering
July 1, 2012 – May 1, 2017
Columbia University
Adjunct Associate Faculty
September 1, 2025 – Present
New York, New York, United States · Remote
Nike
Lead Data Scientist, Product Creation & Merchandising Data Science
February 1, 2025 – Present
Remote
Nike
Sr. Data Scientist, Product Creation & Merchandising Data Science
July 1, 2023 – February 1, 2025
Remote
Nike
Data Scientist, Product Creation & Merchandising Data Science
January 1, 2023 – July 1, 2023
Remote
Nike
Data Scientist, Sport Activity Data Science
July 1, 2022 – January 1, 2023
Remote
Nike
Graduate Data Science Intern, Sport Activity
June 1, 2021 – August 1, 2021
Evanston, Illinois, United States · Remote
Stats Perform
Artificial Intelligence Intern
June 1, 2019 – August 1, 2019
Chicago, Illinois, United States · On-site
Stats Perform
Artificial Intelligence Intern
June 1, 2018 – August 1, 2018
Chicago, Illinois, United States · On-site
Northwestern University
PhD Candidate, Electrical Engineering
September 1, 2017 – July 1, 2022
Evanston, Illinois, United States · On-site
Girls Who Code
Summer Immersion Lead Instructor, BlackRock
May 1, 2017 – August 1, 2017
New York, New York, United States · On-site
NYU Tandon School of Engineering
Research Assistant, Electrical Engineering
May 1, 2016 – May 1, 2017
Brooklyn, New York, United States · On-site
NYU Tandon School of Engineering
Research Assistant, Applied Mathematics
May 1, 2015 – August 1, 2015
Brooklyn, NY · Hybrid
John Deere
Product Engineering Intern
May 1, 2014 – August 1, 2014
Moline, Illinois, United States · On-site
Generative AI Application Development
Databricks
June 24, 2026 – Present
Generative AI Solution Development
Databricks
June 24, 2026 – Present
Machine Learning Operations
Databricks
June 24, 2026 – Present
Generative AI Application Evaluation and Governance
Databricks
June 24, 2026 – Present
Machine Learning Model Deployment
Databricks
June 24, 2026 – Present
Data Preparation for Machine Learning
Databricks
June 24, 2026 – Present
Cultural Fit Analysis
The candidate demonstrates a strong academic background and a consistent career progression within a major company (Nike), indicating stability and a growth mindset. Their involvement in Girls Who Code suggests a commitment to community and mentorship. The diverse application of ML skills from neuroimaging to product recommendations and sports analytics shows adaptability and a broad interest in problem domains. However, the lack of explicit project diversity outside of Nike and academic research might suggest a more focused rather than broadly diverse project portfolio, which could be a minor consideration for cultural fit depending on the target company's emphasis on varied industry exposure.
Soft Skills & Operational Fit
The candidate's experience as an Adjunct Associate Faculty and Lead Instructor suggests strong communication and mentorship skills. Their work at Nike involving cross-functional collaboration and multi-annotator evaluation frameworks indicates an ability to work effectively in teams and manage complex projects. The PhD research also highlights independent problem-solving and project management.