Nearly 18 years can hold a lot of change. For Deepti Nayak, that’s meant growing alongside new technology, shifting markets and evolving regulations. We asked her what has kept the work interesting and what she’s learned about building a career in commodity trading and risk management.
"I've stopped counting years. I’m actually a boomerang. I joined back in 2006, then left after a year to get married, tried living in a new city and then I came back to Publicis Sapient. Now I'm approaching 18 years in energy and commodities, having gone from Java developer to leading teams managing the full technology stack behind commodity trading operations. So that means trade capture, risk monitoring, settlement, and invoicing.”
"The full trade lifecycle. Front office, middle office, back office. Basically everything from the moment a trade is captured to the moment it settles and invoices. Understanding how a trade moves from inception to close across every system it touches, is what separates people who can maintain a platform from people who can build one that survives what the market throws at it. What also sets the work apart is the exposure. People here don't remain confined to a single function. The technology changes, the platforms evolve, but the complexity of moving a trade from inception to settlement and of keeping systems aligned under exponential business growth, remains a problem worth solving."
“Gradually, then completely. The role evolved from being purely technical into techno-functional meaning, system expertise and domain knowledge running together rather than in separate rooms. I also believe that technology is just a means to solve a problem. When you see technology as a tool rather than the destination, it stops being a barrier. That is what keeps me curious and ultimately why I moved beyond coding and into this space.”
“There are two distinct problems AI can solve in this space. The first is the business problem. Take contract documents, when energy companies sign agreements with counterparties, you're often looking at legal documents running to 1,500 pages. Reading, summarizing, extracting the commercially relevant terms, all of that can be solved by AI right now. The second problem is delivery speed. Faster coding, code quality, industry standards, logging, AI accelerates the technology side of implementation. In commodity trading, the implementation timelines are long. These aren't e-commerce deployments where a product that doesn't ship today becomes irrelevant tomorrow. The window is measured in years. Which means the AI value proposition in ETRM isn't primarily about speed, it's about what the system can do that it couldn't before.”
“I was part of a team building a new trading operation entirely from the ground up. Typically, a project like that would take something like 3 years. Midway through the project, COVID hit. The world stopped and we were immediately thrusted into remote work. We did everything training, testing, go live support, all of it with a userbase that never touched the platform before. We delivered it in 15 months despite all of the challenges COVID brought. This is the sort of delivery that only happens when the right people come together and refuse to let it fail and that’s why it’s such a memorable project for me."
Eighteen years, one constant: keep learning, keep building and keep following the problem.