January Innovation & Technology Culture

Your take on “moving fast” — in one line?

Speed comes from ruthless scope discipline: Ship the smallest thing that generates real user signal, then compound from there.

What metric shows speed without burnout?

Percent complete versus planned, and tracking unplanned work separately. Teams burn out when “sprint complete” hides the reality of constant firefighting. At January, we measure both planned delivery and unplanned interruptions, such as bugs, incidents and scope creep. When we see planned completion stay high and unplanned work drop, it means the system is healthy; engineers are spending most of their time building instead of context-switching. We review those trends in quick retros every sprint so we can fix the friction, not just push through it.

What habit keeps launches safe?

Ruthless scope protection and clear go/no-go criteria set well before launch. At January, we protect timelines by front-loading product requirements and problem clarity upfront, then defending that scope against feature creep. Every launch has a decision owner who is empowered to make trade-offs without waiting on committee consensus. We map dependencies so blockers surface early, not at launch. Safe launches mean high confidence in the product we’re shipping because we’ve implemented observability to provide that confidence and had clarity around the problem space, so ownership could be exercised at all levels — speed through systems, not heroics.

What project are you most excited to work on in 2025? 

I’m excited to continue leveraging improvements in LLMs to help our borrowers. I know, everyone says that, but we’re applying it in ways that directly improve financial stability for millions.

One of my team’s responsibilities is improving our contact center’s tooling — so when we make our agents’ jobs easier, we’re also helping consumers get better assistance. Every week, we spend time shadowing our agents, getting visibility into the challenges they’re facing and seeing how our improvements help them. The tight feedback loop helps keep us focused on what matters.

In 2025 we have exciting plans to surface smarter, context-aware insights to our agents, helping them guide consumers through their financial journeys. A few weeks ago I worked on a hackathon project that used an LLM to generate relevant insights, and present it to the agents at the moment they need it. But that was just a demo. Next quarter, we’ll take that foundation and build something production-grade, with the right guardrails and oversight.

What does the roadmap for this project look like? What challenges or blocks do you anticipate? How do you envision overcoming those challenges?

We are still in the brainstorming phase of this project, so it isn’t fully spec’ed out yet. To make a more robust, production ready version of our hackathon project, we’ll need to work with our talented analytics team. They plan to use an ML model to add context to consumers’ profiles. That foundation will help us prioritize the most relevant insights for agents so they aren’t overwhelmed with noise. 

We may leverage this with a CDP and telephony integration to give agents actionable information in real time. The goal isn’t just to solve consumers’ current issues, but to proactively equip agents with context that helps borrowers navigate their debts more effectively.

One technical challenge will be scaling efficiently — January works with millions of consumers, so we need to make sure we surface insights where they have the most impact. A broader challenge is driving adoption among agents. We don’t want to disrupt their workflows or slow them down. To get this right, we’ll work closely with agents from day one and iterate alongside them before rolling it out more broadly.

What in your past projects, education or work history best prepares you to tackle this project? What do you hope to learn from this work to apply in the future?

Last quarter, I led a project that was probably the most fulfilling technical work I’ve ever done — an AI-powered tool that assists consumers with January’s application. That work built foundational AI infrastructure, which we’re now using to accelerate future projects. We set up systems and services for regression testing, guardrails against prompt injections, monitoring quality in production and gathering insights from consumer interactions. The investment lets us move faster and more safely when deploying AI-driven improvements.

My team worked so well together — we collaborated with design and various stakeholders, aligned on a plan, split up the work, and shipped a tool that’s now helping consumers every day. All in less than a quarter! Every week we review how it’s being used, and it’s awesome to see our work making a difference. I’m excited to tackle even more ambitious projects this year, building AI-assisted tooling that helps us execute on our mission of helping consumers achieve financial stability.