With over a decade of experience across various data and engineering roles, the main theme of my career has been bringing data-intensive applications to production. This has included anything from solving isolated data problems to building systems that serve millions of users. With a proven capability to work independently and in teams, lead and mentor colleagues, and communicate with both technical and non-technical stakeholders, my focus is always on delivering business value.

My experience and formal education fall under three key areas:

  • software engineering (15+ years; Computer Science BSc)
  • data science / engineering (10+ years; Artificial Intelligence PhD)
  • tech leadership (5+ years with startups and scaleups)

⚡ New! These days, I provide independent consulting services around Data & AI, focusing on startups and scaleups in the climate tech and nature-positive sector. See my consulting page for details, or head directly to my contact page if you have a problem you want to discuss.

Past work examples

Let’s go deeper with a few highlights from my work:

Outcomes beat job titles

One of the downsides of working in an ever-changing field and accumulating a broad range of experiences is that it’s hard to summarise with a concise title. For example, being a data scientist used to imply having strong software engineering skills, but this has changed over time. It’s a similar story with the decline and rise of artificial intelligence. In an ideal world, I’d be able to let my work speak for itself. In our world, people search for keywords and have different understandings of concepts, e.g., they may want “an AI solution” to a problem that can be solved with deterministic software engineering. Or they may believe they need an AI Engineer rather than a Data Scientist, when a few years ago it’d have been the opposite (as I’m writing this in 2023, you could replace the words data science with artificial intelligence across my historical posts and much of what I wrote would still hold).

Anyway, whether you’re trying to navigate Data & AI terminology or solve specific problems, I can probably help. As noted in my consulting page, my aim is to get to the root of business problems and iteratively implement pragmatic solutions. The taxonomy of Data & AI professionals is only relevant if I’m helping you hire a team.

Venn diagram showing different MLOps-related roles: Data Scientist, ML/MLOps Engineer, Backend Engineer, Data Engineer, DevOps Engineer, and Software Engineer

A subset of roles I’ve performed in one way or another.
Source: Machine Learning Operations (MLOps): Overview, Definition, and Architecture.