How flexible are the requirements in data science job postings?

Video: Why you need to be concrete in your resume

Good morning and happy New Year!

This is the SharpestMinds newsletter, where I share

  1. A video of a data science job search tip,

  2. A conversation or post from our internal Slack, and

  3. A story of a mentee who’s just been hired.

The video: being concrete in your resume

As I tweeted recently, hiring managers look for concrete evidence of competence in your resume. Despite this, it’s common to see skills listed without evidence in resumes (e.g., “leadership skills” with no context).

Hiring managers largely ignore claims without evidence. What kind of evidence do they want? Numbers, stories, or links to things you’ve built or written. In other words, proof points that are hard to fake. I go into more detail in this video:


The Slack update: how to tell if you qualify for a data science job posting

This week’s Slack update isn’t a conversation—it’s a great standalone post by one of our mentors, Ryan Kingery.

Ryan is answering the question: If you see a data science job posting that asks for 2 years’ experience—and you don’t have any experience yet—is it worth applying to it? Or would you just be wasting your time?

And how strict are the requirements in job postings, really?

Here’s what Ryan had to say:

If it doesn't say “senior” in the title, it's generally implied to be entry level. In my experience, the posted requirements are always overly optimistic. You can usually talk 3 years down to 1 year with little work, and 5 years down to 2-3. Same with education: I've talked with multiple companies that technically require PhDs, down to just a masters.

Also, note that the earlier stage a startup, the more flexible roles tend to be. While a posting may say one thing, you can often talk them into something completely different if you can get them to see you as valuable. I know a guy who came into my last startup as a data engineer technically, but ended up doing customer success work instead out of interest, and now he's doing product management after only a few months.

— Ryan Kingery

Ryan is based in the San Francisco Bay Area, but in our experience almost all North American job postings have their requirements exaggerated to some degree, compared to who they’d actually be willing to hire. The trick is to go to the hiring manager rather through a recruiter.


The hire: Suchitra Ganapathi 🎉

Finally, congratulations to Suchitra Ganapathi, who got hired as an ML Engineer at Johnson & Johnson!

Suchitra is based in Seattle, which is one of the most challenging job markets in North America—mostly for cultural reasons. It’s possible to make headway anywhere if you persevere though, and Suchitra certainly did. She’s one of the fastest learners we’ve come across, and she was 100% determined to get a job in data science. Congrats, Suchi!

Check out the full announcement on LinkedIn here.


Have a great week! ✌️

— Edouard

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