Chris is a founding partner of Hypergolic, a machine learning product consultancy. He’s passionate about helping product teams to make the best use of machine learning, minimising time-to-value and maximising productivity. He’s been a product manager for 10 years, most recently as Head of Product at Arkera, a financial intelligence company. When he’s not helping clients, he blogs about the unique needs of product management in machine learning.

What’s the story of your career so far?

I started out in tech in July 2008, a few short months before the crash. A large US fintech company was expanding in London and creating a new governance product for individual investors, and the product was really appealing to me. The team was small and organised like a startup, and I fit into a product-like role, working in a Lean framework before the release of the eponymous book. I immediately realised that product management was the role for me and buried myself in it.

My career has been pretty varied from there, though primarily building products for banks and other parts of the financial ecosystem. I’ve launched a few fintech products now, some of them have been really successful. Over the last few years, I’ve worked more in emerging tech, with a particular focus on Machine Learning since joining Arkera in 2019.

What do you love most about what you do?

I think like many product managers, I’m a natural problem solver. I love digging deep into how people work, understanding new domains, their processes and businesses, and uncovering interesting problems. In B2B product management in particular there can be a real cut-and-thrust to a client conversation, as you both try and get information out of each other.

I also find the process of solving problems really fun. I like working with strong, technical teams, tackling difficult problems with tight deadlines. The process of navigating difficult problems is very satisfying. I think you need some pressure to really perform at your best.

What’s the most important lesson you learned in 2020 amidst all the Covid-19 disruption?

The importance of maintaining relationships. This has taken lots of different forms. Early on in the first lockdown, we were very deliberate in how we maintained communication, with at least a daily Zoom check-in, meetings always held with video, conversations over Slack. I think with just a little effort you can maintain good-quality dialogue with the tools like this, and of course, there are many more collaboration tools out there. There are lots of ways to keep a sense of camaraderie with virtual tools, and this extends well to working with other teams and organisations, who are naturally working at a distance to you.

Beyond just the technology though, we no longer have the “serendipity effect” of meeting people, contacts old and new, in passing. At first, I didn’t really think about this, but now I’m trying to be more strategic in maintaining relationships. Even if it’s just the odd email to say hello, I think it’s important to remind your contacts that you’re still around.

As far as you can predict, what’s on the cards this year for you and your business?

Machine learning is in a really interesting place right now. It’s barely over the hump of the hype curve, and many companies are using the technology only in very speculative new products, without integrating data science into their existing teams. But in a lot of industries, we’re seeing individual companies who are repeatedly and rapidly delivering valuable data products to production, and they are starting to build an edge. I think in the next 12 months we’ll see these companies pull further ahead, and others will be playing catch-up.

At Hypergolic, we’re focused on helping teams get better at building and shipping data products. With the rise in demand for data scientists we expect to spend a lot of the next 12 months coaching teams, and I expect a lot of those teams will be academic-heavy and commercial-light as demand for data scientists outstrips supply. We’re also seeing a lot of companies look more closely at their machine learning pipeline, and looking for help in developing a more comprehensive ML Ops strategy, so I expect to be spending a lot of time working with companies on that.

If you could go back and do it all over again, would you choose a different career path and why?

No, it’s all been an adventure, I’ve learnt a lot, and I enjoy what I do! If I ever get tired of it I’ll retire to a vineyard in France but right now I wouldn’t want to do anything else.

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