Scala catches a lot of unwarranted flack. Rather than stretch themselves, some developers with short attention spans lash out and run away to more familiar languages. That’s fine. The problem is that they leave a trail of online discouragement in their wake, which is unfair to Scala as a language and makes interested developers question the value of the language.
A change made by an editor is indistinguishable from a change made by a developer. No magic markers as to what can and can’t be modified by hand; editors able to follow project style guidelines and produce clean diffs preserving formatting and comments. The aim is not to minimize the role of developers, but to make developers more powerful by scaling them up via a polite, invited, automated helper that follows their preferences.
If there is pressure for the data science team to make products look great even when evidence doesn’t support that view, then leadership is rotten. Teams must be able to report negative results confidently, otherwise everyone will lose trust in positive results. Data science teams need access to decision-makers with high leverage questions, and those decision-makers must have an honest relationship with data and evidence. One good proxy for this is whether there is demand for the data science team’s involvement and that leaders can rapidly identify how data science helped their team succeed. The final questions, 12–14, try to catch any of these issues.
Internal team processes (covered by questions 9–11) ensure the team is doing the kind of high quality research work that builds and maintains trust in the organization. Validating the work of a data scientist is out of reach for most of the team’s customers, so it is the responsibility of the team to commit to documenting their work, putting it through strenuous peer review, and evangelizing results. It should go without saying, but controlled experimentation is the most critical tool in data science’s arsenal and a team that doesn’t make regular use of it is doing something wrong.
Great data science work is built on a hierarchy of basic needs: powerful data infrastructure that is well maintained, protection from ad-hoc distractions, high quality data, strong team research processes, and access to open-minded decision-makers with high leverage problems to solve.
Tim Hunt also mentions that men and women are constantly falling in love with each other within the lab. This is true; I used to identify as homosexual before working with a group of bearded men and a set of phallic pipettes turned me straight. Once that happened, I couldn’t stop falling in love with every man I met. One time I fell in love with Ernest Rutherford because there was a picture of him in our lab. Another time I dated a coworker for three months before realizing he was actually just an extra large lab coat with a smiley face drawn on the lapel. (He currently holds a tenure-track faculty position at Harvard University.)