I have a stealth recruiting talk that I give for machine learning at Xpanse. It goes like this:

• If you want a real mission in your work, cybersecurity can deliver.
• My realm of cybersecurity is impossible without AI.
• Doing this job means solving cool, hard problems.

I pretend the talk is all very objective and about teaching you stuff (which hopefully it also does). I also hint at a lot of the problems that I’ve worked on and solved the past few years. Technical people really like being shown problems and getting to chew on them (which is convenient, because I’m not comfortable publicly sharing how I solved these problems!). And I’m sure it helps that I’m earnest – the slides really are what I love most about my job. The talk works, too. We get solid applicants from it.

I was inspired by a talk I saw from Stitch Fix a number of years ago. I have really minimal interest in clothing… but after hearing them give a technical talk about the problems they were solving, I became convinced they were doing really neat modeling and would be worth considering in a job hunt. Pretty effective. So I tried to channel that insight.

The slides I’m linking here conclude with a harder sell than I usually give, as well as some cross-team Palo Alto projects, because I revised this version for an explicitly recruiting context. (One of our senior recruiters had seen me give this talk at the Lesbians Who Tech conference, and he asked me to give it again in a different context.)

Body language can indicate state of mind. Being familiar with body language tells can help people read a room, avoid closing past the sell on a negotiation, and become more self-aware. I wrote and delivered a short orientation to Comfort, distress and dominance: Reading body language as part of a non-technical skills development series within an established team. It is framed as three 2-3 minute topic introductions followed by 5-10 minutes of small group moderated discussion.

Last year I went looking for an “inductive-deductive loop” image (I was trying to convince stone-cold scientific method biologists that it really is okay to start science from observations), but I couldn’t find anything close to the simple diagram I was envisioning.  So, I drew my version on a Post-it note, and I’m sharing it now for posterity and for Google Images.

My talking point here is that scientific inquiry is both inductive and deductive.  Although many disciplines privilege a single type of reasoning, it’s better to integrate both approaches. With a circular view, we are free to enter problems where it’s most straightforward to start them — exploring the data, taking hypotheses or patterns to their conclusions, or considering how known theories might manifest — knowing that we’ll do a complete investigation in the end.  We trace as far as we can through the loop, verifying our interpretations through multiple methods.  Sometimes we cycle around the loop multiple times.

For instance, if you’re heavy on data and light on abstractions, you might start by trying to find patterns in the observations.  Once you identify some patterns, you formalize those patterns into a theory.  Given theory, you can generate some hypotheses based on the implications of that theory.  You then collect more data to disprove those hypotheses.  The new observations might suggest new patterns, starting another round of the loop.  You don’t limit yourself to collecting data only to disprove hypotheses, though — you also look at data that hasn’t been deliberately collected under the premises required by your hypotheses.  By looking at all the observations, you can start to investigate when the premises themselves hold.

The inductive-deductive loop is the structure of scientific inquiry.