Nico Manzonelli

  • Section 1
  • Student

Activity Feed

On April 20, 2022, Nico Manzonelli commented on People Analytics and Hybrid Work :

I agree, people analytics often feels very reactionary, but we have the opportunity to be more proactive with changing workforce norms. Like you said, people analytics can inform decisions on employee interactions (like little check-ins) to how the physical office is laid out. Another thing that I think is special about being proactive with analytics is that provides more transparency. Now, I feel like managers can say, “we made decision XYZ because the stats told us ABC” rather than, “we made decision XYZ because we thought it was best.” So, employees going back to the office might get a better answer to questions like “Why do I need to zoom with Pam today if she’s coming into the office tomorrow?” Although the response “because the stats say that if you don’t talk to Pam today she’ll be more likley to demonstrate signs of mental distress” seems off-putting, I’m sure there are better ways to communicate analytics-driven decisions to individual employees. Effectively communicating these proactive decisions will be important moving forward.

On April 19, 2022, Nico Manzonelli commented on From Blue Collar to New Collar :

Hi Grace, I think you ask a lot of interesting questions concerning skill assessments. Here’s my perspective on the college v. self-taught aspect as I’m finishing up my first year in the data science program. A motivated person could learn everything technical that I’ve learned thus far from Medium articles and YouTube videos (which I often lean on anyways). Personally, I need the formal schooling environment to learn this stuff, but the information is definitely out there. After I graduate, I’m sure there will be self taught data scientists out there who could code circles around me which makes it harder for hiring managers to differentiate between me and Candidate X who is self-taught prodigy. I think this is where coding interviews for “new collar” jobs and other analytics/dev roles help distinguish the real players. Also, coding interviews take away some of the pressure to establish training programs because they can just pile their training objectives into one assessment. Seems like a simple solution, but it raises more questions. What if the best coder doesn’t fit the culture? Is the best coder always the best for the job? etc. Additionally, coding interviews really only work for jobs that involve slinging code on the daily.

Hi Georgiy, I like your take. As a sports analytics geek, I never really considered how teams recruit technical talent. Because sport analytics has to do with setting the lineup, optimizing play-calling, valuing players and managing salary cap, the focus is always on the talent and team performance which means these analytics teams really never get any love. I’m actually not even sure if the GM on most teams hires data scientists, or sets the vision for the analytics team. It’s likely that interaction between Billy Beane and Peter Brand (Brad Pitt and Jonah Hill) in Moneyball probably isn’t how it actually works in most organizations. Then, you raise another good point: as an added complexity football is hard to model. Modern tech like non-invasive player and ball tracking helps, but still modeling football requires some serious data science skills. It will be interesting to see how clubs manage and recruit off-the-field talent moving forward.