JLJinghang LiWork / Applied AI
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About / Jinghang “Jacky” Li

A data person.
A practical problem solver.

I use evidence to understand a problem, judgment to choose a path, and collaboration to make the result work in practice.

  • Senior Data Scientist
  • Chicago
  • Applied AI
  • Ph.D.
Portrait of Jinghang (Jacky) Li
Jinghang Li, Ph.D. · Chicago, Illinois

I’m Jinghang Li—Jacky to friends and colleagues—a Senior Data Scientist based in Chicago.

I don’t chase one supposedly optimal answer for every situation. I look at the data, listen to the people closest to the problem, and weigh what a team can realistically deliver. The best solution is the one that fits the circumstances and moves the work forward.

My path began in mechanical engineering and grew into operations research, a Ph.D. at the University of Illinois Chicago in 2021, and applied AI. Research taught me to test assumptions. Production taught me that quality, time, risk, and adoption all belong in the same decision.

Today I lead AI product initiatives while staying hands-on with the technology. I work with engineering and product teams across the US, China, Malaysia, and Singapore, creating shared context so different perspectives can move toward the same goal.

Read my experience →
What I bring

Technical depth.
Broader responsibility.

Product judgmentStart with value

Define the outcome, weigh trade-offs, and choose an approach that fits the workflow.

Technical depthResearch to delivery

Ph.D. training, production AI, and hands-on work with Python, SQL, AWS, Azure, PyTorch, and TensorFlow.

People leadershipBuild shared context

Cross-functional collaboration, engineer mentoring, technical hiring, and team innovation.

Working principles

How I choose
a way forward.

01 / Understand

Question the framing

What problem matters to the user? What does the evidence show? What would a useful result change?

02 / Decide

Fit the situation

Balance quality, delivery time, operating risk, and team capacity. A bigger rebuild is not automatically a better answer.

03 / Deliver

Learn with the team

Make assumptions explicit, give people room to contribute, and use real outcomes to guide the next improvement.

Beyond my own code

Progress is
a team effort.

Some of my most meaningful work is helping other people do theirs.

  • Mentoring junior ML engineers and sharing context behind technical decisions.
  • Participating in multiple rounds of technical interviews to help build the team.
  • Helping lead annual internal AI hackathons, working with leadership on themes and guiding participating teams.
  • Bridging product and engineering perspectives across time zones and cultures.
Next chapter

See the work
in motion.

Explore projects →