Job Titles on Harlan D. Harris

Staff Data Scientist: Comments on Will Larson's Staff Engineer Book

harlan@harris.name (Harlan Harris) — Fri, 27 May 2022 00:00:00 +0000

I recently read Will Larson's excellent book Staff Engineer: Leadership beyond the management track. Larson covers the individual contributor (IC, not management) roles that software engineers fill after they are promoted past Senior Software Engineer, with titles like Staff and Principal ("Staff-plus"). In the book, he synthesizes his own experience and the experiences of a number of other Staff-plus engineers, and provides great insights into how to get promoted to that level, and how to succeed at it. Great book -- you should read it.

Building a Complementary Data Science Team

harlan@harris.name (Harlan Harris) — Mon, 23 Nov 2015 00:00:00 +0000

This post was originally published on Medium

I’m the Director of Data Science at EAB, a firm that provides best-practices research and enterprise software for colleges and universities. My team is responsible for the predictive models and other advanced analytics that are part of the Student Success Collaborative product that’s used by academic advisors and other campus leadership. We’re hiring data scientists, and I wanted to publicly say a few things about the roles we have advertised. (Note that EAB is part of a public company and is in a competitive market, so there are obviously things I’m not saying!)

On "Analytics" and related fields

harlan@harris.name (Harlan Harris) — Fri, 15 Apr 2011 00:00:00 +0000

I recently attended the INFORMS Conference on Business Analytics and Operations Research, aka “INFORMS Analytics 2011”, conference in Chicago. This deserves a little bit of an explanation. INFORMS is the professional organization for Operations Research (OR) and Management Science (MS), which are terms describing approaches to improving business efficiency by use of mathematical optimization and simulation tools. OR is perhaps best known for the technique of Linear Programming (read “Programming” as “Planning”), which is a method for optimizing a useful class of mathematical expressions under various constraints extremely efficiently. You can, for example, solve scheduling, assignment, transportation, factory layout, and similar problems with millions of variables in seconds. These techniques came out of large-scale government and especially military logistics and decision-making needs of the mid-20th century, and have now been applied extensively in many industries. Have you seen the UPS “We (heart) Logistics” ad? That’s OR.

"Data Scientist" and other titles

harlan@harris.name (Harlan Harris) — Sun, 13 Feb 2011 00:00:00 +0000

Neil Saunders has an interesting (to me) blog post up this morning, with the title “Dumped on by data scientists.” He uses the use of “data scientist” in a Chronicle of Higher Ed article to rant a little bit about the term. For Neil, it’s redundant, as the act of doing science necessarily requires data; it’s insulting, as if “scientist” wasn’t cool enough and you have to add “data”; and it’s misleading, as many people who call themselves “data scientists” are actually dealing with business data rather than scientific data.