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Celebrating Year One: Q&A with Co-Founder Jane Tran
Behind the Scenes

Celebrating Year One: Q&A with Co-Founder Jane Tran

We chatted with Sixfold's Co-founder & COO about the first year of Sixfold and what's next.

5 min read
Maja Hamberg

We had an opportunity to chat with Sixfold's Co-founder and COO Jane Tran about the company’s amazing first year and the vision for the years to come, as well as her career journey, giving back, and tips on running a fast-paced AI startup.

What was your first job and how did that influence your career?

My very first job ever was as a cashier at an Italian deli around the corner from my parents’ house.

I think everyone should do a service job because it emphasizes the importance of good customer service like how to treat people. I also learned how to be quick because it's New York. New Yorkers don’t like to wait for their Bacon, Egg, and Cheese.

Walk us through your career journey from the Italian deli to co-founder and COO at  Sixfold.

I’ve had a good mix of enterprise and startup experience. I started my career at JP Morgan as part of their rotational analyst program. One of my last rotations was with the Turnaround & Process Improvement team for the Chief Information Officer—that’s where I fell in love with tech. I worked on really cool projects like improving the eDiscovery process and improving data governance. I had a blast. 

After that, I spent several years at Marsh and MetLife working with the CIOs on different strategy and planning projects before I decided to give startups to go. I was on the founding team at Unqork where I was Head of Solutions before becoming COO.

When I decided to leave Unqork, I kept in contact with Alex [Schmelkin, founding team at Unqork, and co-founder of Sixfold]. When he came up with the idea for what would become Sixfold, he asked me to join him to get this idea off the ground.

What does the name Sixfold mean and who came up with it?

So, all kudos due to Alex’s daughter Nina Schmelkin for that! She was doing a project for school around patterns. A sixfold pattern is considered one of the most interesting, naturally occurring patterns—snowflakes are a sixfold pattern. And so, when it came to choosing a name for the company, we were thinking about AI and the role that patterns play, and “Sixfold” seemed like an ideal fit.

Sixfold just turned one year old. How would you describe year one?

A ton of fun! We’re building really tangible use cases using cutting-edge tech. This first year has reinforced the importance of anchoring your work in first principles. AI is obviously super hot and evolving at warp speed, but we can't ignore the things that support great software development and great user experiences. That meant getting that foundation and discipline in place while at the same time making room for extensive R&D and a ton of iterations. 

We learned a lot. We tweaked a bunch. And I think we found product market fit. Our early customers are already starting to see value, and I’m really excited to see where that grows this year.

Do you have any notable “wow” moments from the first year?

Yeah, when we delivered our first end-to-end underwriting pilots and heard the underwriters say “we were able to complete this task in a fifth of the time.” I particularly love hearing how much they trust the tool.

Hearing that first positive user feedback feels like a major achievement! And, obviously, the recent closing of our Series A funding round.

Who are your role models and how did they influence your career? 

My parents. My mom runs a small business in kitchen supplies with her siblings — it was one of those things where they just sort of fell into it. They knew they could offer a really good product and create a fit within the market. They understood what customers wanted and knew that they could manufacture it. So they just went for it. That takes a lot of bravery. On the flip side, my dad hung wallpaper for a living. He's retired now, but he had that hard work ethic and true care for his craft. He developed a reputation for excellence and really worked his way up.

I think the combination of entrepreneurship, work ethic, and quality very much influences who I am.

A common conversation for startups is balancing “the need for speed” with employee happiness. How do you build that balance into the company culture? 

Unfortunately, I don't have a magic formula. I would say that from the get-go, Sixfold’s three founders — Brian, Alex, and myself — anchored ourselves on our Mission and a handful of operating principles like putting the customer first and being direct while being kind. We try to surround ourselves with people who share those principles so that naturally becomes the culture of the company. 

Jane together with Alex Schmelkin (Co-founder & CEO) and Brian Moseley (Co-founder & CTO).

The three of us really care about who works for us and how we all work together. Sometimes we may not have the best balance, but we always strive to be better. As founders, we care a lot about our work, but we also care deeply about family, friends, and life outside work. We understand that people who work with us have the same need for a balanced life outside work.

Do you have any tips when it comes to hiring?

Instinct is a huge part of it. It’s also helpful to have a great HR team in place — kudos to Marie [Sixfold’s HR Business Partner] for doing a lot of the initial groundwork, so by the time a candidate gets to me, they fit a lot of our criteria for that role. From there, a lot of it just comes down to just instinct. Ask yourself if they'll fit within the culture of this company.

Tell us about your mentoring work for different startups and organizations.

I'm on the board of directors and co-chair for an organization called Womankind. It helps survivors of gender-based violence in New York, with a focus on the AAPI community. They've been around for more than 40 years. They started with a single hotline for the NYC area, but have expanded to serve thousands of women every year across the US as one of the few true end-to-end organizations. So families get to stay together. They get legal help. They get job placement. Their kids have a safe place to be while they're figuring out, you know, the next steps. I'm really proud to be part of that organization. 

I've also mentored and advised a few other early-stage startups that I think are doing something new and interesting. This also helps me to understand what else is out there in the ecosystem. I’ve mostly been focused on B2B enterprise throughout my career, so I like learning about retail or other sectors.

I love being around people who are building interesting things. It’s super fun and it can be super informational too.

What are your top work tools that you feel like you couldn’t do without?

I don't do a lot of the things that “productivity hackers” do. I would say Apple Notes and Google Tasks are central to my workday. I keep extensive notes on my meetings— so that’s a lot of Apple notes. And then, for the important things with a deadline, I'll set up a Google task or calendar reminder. And that's how I organize.

What are you excited about for Sixfold’s second year? 

This year will be about continuing to mature our product and getting a lot of new customer use cases live.

We're working with a lot of great people and a lot of great customers. I’m looking forward to showcasing what we can do within this market and at this fidelity — a year ago, I don't think anyone would have thought that we’d be able to do what we’re doing, so I can’t wait to see what the next year or two will bring!

Want to work with Jane and the rest of the Sixfold team? Check out our career page
Building the AI Future With Sixfold’s Head of AI/ML
Behind the Scenes

Building the AI Future With Sixfold’s Head of AI/ML

We talked with Sixfold’s Director of AI/ML Ian P. Cook, PhD about his career path and his role at SIxfold.

5 min read
Maja Hamberg

We talked with Sixfold’s latest hire, Head of AI/ML Ian P. Cook, PhD about his career journey, how emerging technology will overcome long-standing industry challenges, and his new role as Sixfold’s data science leader.

Welcome to the team, Ian! Walk us through your career journey up to this point.

I caught the bug for quantitative work when I was in grad school studying Public Policy at the University of Chicago. After graduation, I worked in various policy analysis roles, including at the RAND Corporation as well as doing work for all the major defense agencies and other federal orgs.

While doing policy work, I was simultaneously pursuing my PhD at the University of Pittsburgh for Political Science. My trusty “dad joke” is that I wasn’t smart enough to do grad school just once. As part of my research, I taught myself Python and found that my skills in econometrics translated well to the then-exploding field of data science. After I received my degree, I worked with startups and went from building tech products to building tech teams as Chief Data Scientist with a GovTech company and Chief Technical Officer for a business analytics SaaS.

Are there any tech projects you’re particularly proud of?  

One of my favorite projects was a matching & recommendation tool for patients. A significant predictor of poor health outcomes is missing doctor appointments, but as it turns out people don’t just “miss” them, they avoid them when they’re not happy with the style or approach of a doctor or practice. This was a problem that me and my team believed could be engineered around. 

I oversaw the team building the machine learning functionality for a web tool that assessed both patient preferences and the style of healthcare providers and then turned that into a kind of Match.com for healthcare. Not only was it fun to build, but I’m particularly proud to know that it helped keep people getting the care they need.

Why Sixfold?

Sixfold immediately piqued my interest. The company is attacking a clear and sizable pain point for a well-defined customer (anyone with startup experience will tell you that’s not always the case). Plus, they’re doing it with what I see as generation-defining tech—LLMs are amazing in their own right, and having the opportunity to put them to practical use is an exciting opportunity. After meeting with the team and the leadership, I knew that this was where I wanted the next step of my career to be. Excited to get to work with an amazing crew—tip of the hat to Stewart, Drew, and the whole engineering team!

Everyone’s talking about LLM-powered generative AI these days. From your perspective, what are the most intriguing possibilities and potential risks of this emerging generation of tech?

I sit somewhere in the middle between the extremes of the AI discourse: I don’t think AI will give rise to a post-human apocalypse, but I also don’t believe we can just sit back and toss every hard problem at an AI and implement whatever solution pops out. 

We’re going to see these tools accelerate transformation across every industry. In most cases, that will ultimately be a good thing. However, without clear intention behind how they’re applied and oversight into how they’re trained and deployed, there’s a real risk for these tools to cause harm—unintentional or otherwise. Part of the attraction to Sixfold was their emphasis on applying AI responsibly.

As someone with a strong data science background, what does it mean for data to be useful, not just accessible?

Access is an aspect of keeping data well-controlled—like security measures and access control for personally identifiable information, health records, financial information, and other sensitive material. 

For data to be useful, it has to address real problems and it has to have been corralled in a thoughtful, purposeful manner. Usefulness requires someone to understand the question the data is meant to help answer and to be aware of potential biases—both statistical and human-generated—which might limit the applicability of the data.

How do you see your role as the AI/ML leader at Sixfold?

I see my chief responsibility as empowering underwriters to do their best work ever by augmenting Sixfold’s product with AI-powered tools. Achieving that means supporting the people who are developing, testing, and deploying those tools. Some days that might mean coordinating priorities and ensuring everyone has the information and resources to deliver. Some days it might mean being chest-deep in the code myself. And some days, I’m sure it’ll be a little of both. 

What do you see as the challenges of implementing AI in insurance vs other industries?

I’ll admit to being relatively new to the insurance industry. That said, even a n00b like myself understands that it comes with unique challenges like complying with regulations across multiple levels of government; implementing stringent processes to handle and distribute the personal, closely-held data of both individuals and corporations; and making a convincing argument for change in a well-established industry where many are content using tools and methods that’ve been around for decades.

As a seasoned technologist, do you think there are types of tasks that will always be better suited for humans, rather than machines? 

AI is going to take on the tasks that slow us down. I like to think of it as a bionic-like tool that augments and improves human performance.

It’ll free us up to focus on the most important—and frankly most meaningful and rewarding—parts of our jobs.

I’ll also add this: the better the machines get, the more we’re going to lean on philosophy, the most thoroughly human of disciplines. Discussions about LLMs are loaded with terms like “reasoning,” “thought,” and “knowledge,” which philosophers have been wrestling with for centuries. I’m reminded of the discourse in my philosophy courses around intention and will, which are completely distinct from the mechanistic processes in deep learning architectures. Philosophy is often derided as a field with little practicality, but as a technologist, I see it becoming more practical by the day.

How do you keep up with the latest developments in your field?

A lot of reading! There are tons of great newsletters that cover the field. Ben’s Bites is fantastic, but there are tons of great ones across Medium, Substack, and Beehiiv. I’m also a fan of podcasts like AI Daily Brief, The Cognitive Revolution, and Talking Machines. I like listening to those while doing chores, walking my dog, driving, etc. 

Keeping up with the latest research is always a challenge—it was an issue even back in my PhD days because there were always new papers coming out. Part of me feels lucky to have gone through that back then because now AI is moving at warp speed and it’s even harder to keep up. But I’ve learned to master the art of “informed skimming,” which means quickly reviewing summaries, conclusions, and writeups to find key terms that will tell you if the paper is relevant to the problem area you’re taking on.

What tools do you rely on the most for your work?

I’m a devoted fan of Pycharm for coding. I’ve tried switching to VS Code and other flashy new IDEs when they come up, but I always go back to Pycharm. For note-taking, I stick to Apple Notes. There’s a whole world of “second brain”/knowledge management tools out there for taking notes, but I have to refrain from those (anyone familiar with the word “Zettelkasten” knows the depths of that particular rabbit hole).

I learned during grad school that the more extensible a tool is, the more time I waste fiddling with configurations. Tweaking color themes is not “optimizing my workflow,” no matter how many times I repeat it to myself.

What is the best thing a person can do who wants to pursue a career in AI/ML?

Some requirements are hard to skip over: a decent amount of math, and enough knowledge to turn that math into code.

But you don’t have to be a genius at either one. Learn some matrix math, and then play with those matrices and see how you can apply them in predictive software. Then learn a little more, and implement a little more. The key to mastering any skill set is repetition and perseverance. To parrot that old quote about how one becomes a writer: write.

More specifically, I think there are three things everyone who wants to work in this field needs to know: SQL, Git, and enough of one programming language to be productive. SQL is the language of data: getting it, moving it, storing it, everything—if you can’t get at the data, it’s going to be hard to trust that you can work with it. Git proves that you understand versioning, reproducibility, and collaboration. When it comes to becoming thoroughly fluent in at least one programming language, I’ll sidestep the religious wars about which language is best or most useful and just say that the important thing is becoming really productive in at least one language.

Any fun tech projects that you're working on at the moment (non-Sixfold-related)?

I’m a fly fisher in my spare time, and I use a fly fishing app called onWater Fish. I reached out to the app’s dev team and learned they needed some ML-like support. So I pitched in to try out some new ideas. We’ve been successful in implementing some cool in-app computer vision work that anglers can use to record their catches (and brag to friends) all with one picture. It’s been a great way to apply my skills to a personal passion of mine, which has been truly rewarding. 

How can people follow your work?

I’m on LinkedIn, and try to post regularly on the practical application of AI, the future of work, and whatever else where I might have a useful take. For other social media, I can usually be found by searching @ianpcook.

Want to join Ian and the rest of the Sixfold team on our mission to transform insurance underwriting with AI? Check out our career page

The Journey of an AI Scientist With Stewart Hu
Behind the Scenes

The Journey of an AI Scientist With Stewart Hu

Explore the fascinating career and daily work of Stewart Hu, an AI scientist at Sixfold. Discover how he stays current in the field of automated underwriting.

5 min read
Maja Hamberg

We recently sat down for a quick Q&A with Stewart Hu, AI scientist at Sixfold. Our conversation ranged from his career journey to how he stays current in the field, as well as the tasks on his daily agenda.

Let’s get this started! In your own words, what does your job as an AI scientist involve?

AI scientists engage in a lot of practical work. Despite our 'scientist' title, our roles often overlap with those of developers or research engineers. In fact, over 50% of our tasks are typical software engineering activities. We develop software grounded in foundational models, employing a range of techniques, not just AI.

Previously, AI encompassed anything linked to machine learning, but now it's more commonly associated with large language models like GPT. Our role includes integrating these models into software applications, utilizing models such as GPT-4, and even fine-tuning our custom models. Additionally, we apply both traditional machine learning and deep learning methods. This involves creating classifiers with techniques predating neural networks, like gradient boosting machines or random forests. At our core, we are software engineers crafting machine learning algorithms to address real-world challenges.

How did you get into the world of Generative AI? 

My fascination with AI really took off with GPT-3's emergence. But it was the debut of the stable diffusion model in August 2022 that truly captivated me. This revelation prompted me to pivot my career towards a tech startup specializing in deep learning and AI.

In the early stages of my career, I worked as a software engineer. This was followed by a ten-year journey in data science, beginning with statistical learning and gradually evolving into machine learning, deep learning, and finally AI. Essentially, I devoted my first decade to hardcore software development, and the next decade explored the realms of data science and machine learning.

Could you give some insights into what's on a typical to-do list for you?

My work is basically divided into three key areas.

Firstly, there's data management: sourcing appropriate data, organizing it properly, and conducting thorough analyses. A major chunk of our time is dedicated to dealing with data - acquiring, scrutinizing, and delving into it.

Secondly, I engage in software development, where my goal is to craft software that's not only reusable but also adaptable to growing complexities. This involves strategic software design to ensure it can be easily scaled up.

The third area is AI, particularly focusing on 'retrieval augmented generation’ . This entails extracting pertinent details from extensive document collections to accurately contextualize models like GPT-4. My day-to-day involves juggling these three components.

How would you distinguish a purpose-built AI tool from a generic one?

AI often gets hyped up with flashy demos requiring little coding. However, Sixfold is a purpose-built gen AI tool, our focus is on crafting solutions that address real-world business problems, not just making eye-catching demos. We use AI to make underwriters work faster, more accurate, and enjoyable. By taking over repetitive tasks, AI allows underwriters to focus on the more engaging aspects of their job.

Our platform is built with a strong emphasis on accountability, not just on interpretability or explainability. This means our solutions cite sources when making recommendations and provide actual source documents for our classifications. It's a practical, business-centric approach that boosts confidence in underwriting decisions.

What excited you the most about joining Sixfold?

Two things particularly drew me to Sixfold. First, the experienced team leading the company. The founders have a proven track record of creating substantial business value, blending tech knowledge with sharp business insight. Second, on a personal level, my wife has been in the insurance industry for over ten years, and I've always found it fascinating. Joining Sixfold presented a chance to dive deeper into this sector. 

It was the combination of the seasoned leadership and the company's expertise in insurance and underwriting that ultimately convinced me to become part of the team.

How do you stay engaged with the AI community? 

My go-to resource is X (formerly known as Twitter), where I've created a list named ‘AI Signals.' This list features over 100 experts deeply engaged in the field, tackling everything from fine-tuning models to enhancing the speed of large language model inference. While some of these individuals may not be widely known, their insights are incredibly valuable. 

Previously, I would follow arXiv for academic papers, GitHub for trending repositories, and Papers with Code to find research papers with their corresponding code. However, X has become my most essential tool. I regularly check updates from my list there to keep up-to-date with the latest developments.

That sounds like a great list! Can we share it with the readers?

Of course, happy to share it - here you go

How can people best follow your work?

I haven't been active on my blog lately, but I do maintain a GitHub repository named 'LLM Notes.' It serves as a practical guide for data scientists and machine learning practitioners. This repository is a compilation of the knowledge and insights I've gathered throughout my career. A few months back, I uploaded a wealth of information there, including lessons learned, common pitfalls, and personal experiences. It's a good resource for anyone interested in the field. 

Thanks for your time, Stewart! We’ll let you get back to your to-do list now.  

If you’d like an opportunity to work at Sixfold, check out our vacancies.