We just released another data science job description teardown for a Data Scientist position at Bank of America.

You can view the job posting here and watch a video of our teardown of this job posting below.

How did it score?

This is a Very Competitive job description and we give it a total score of 8 out of 12.

About Prolego’s data science job description teardowns

Suboptimal data science job descriptions are costing you money

The competition for data science talent continues to increase, and every large company is struggling with recruiting and retention. As our research study, How to Win the War for Data Science Talent, revealed specific actions large companies can take to build data science capabilities.

Unfortunately most companies are not taking even basic steps to improve their recruiting efforts. As a result:

  • Projects are stalled because the company cannot fill data science positions with qualified candidates.
  • The hiring manager and team are frustrated by the poor quality and quantity of candidates.
  • Companies are hiring the wrong people, which results in voluntary and involuntary turnover.

What’s the bottom line? Writing poor data science job descriptions is costing your company money. Fortunately, this is a solvable problem.

Examples analyzed from the candidate’s perspective

We want to help you write data science job descriptions that will increase the quality and quantity of qualified candidates applying for the jobs. While our study provides general advice, we’re trying to make it more relevant with examples. To that end we’ve started doing analysis of job descriptions and sharing them publicly.

Our teardown methodology

Here is our current approach.

  1. We review the job description from the candidate’s perspective. In other words, we don’t rely on inside information the candidate doesn’t have.
  2. We evaluate the job description based on our learnings from the study and our experience. For consistency, we evaluate on 6 dimensions.
  3. Career — What is my career path at this company? What are my opportunities for learning?
  4. Impact — How can I make a difference with my data science skills? What positive impact can I make on the company or society?
  5. Team — Who is my hiring manager? Am I part of a high-performing team of data scientists, data engineers, and product managers working together to deliver value? Or am I the only data scientist on a traditional project team?
  6. Compensation — Does the compensation align with the necessary skills and how does it compare with competing offers?
  7. Activities — What will I primarily be doing? What projects will I be working on? Are there examples of past projects?
  8. Tech stack — What tools will I be using? Do they have the data and infrastructure necessary for me to do my job, or will I be stuck with traditional IT tools?
  9. We assign a score of 0–2 for each dimension and total them. Thus each job description gets a score from 0–12 from which we make a conclusion:

> 7 Very competitive
4–6 Average with opportunities to improve
< 4 Not competitive

Caveat: not a judgment of the hiring team or company

The teardowns are simply our opinion based on how we think qualified candidates will evaluate the opportunity compared to alternatives in the market. They are not judgements of the company or the hiring team. We recognize that the people who write the job descriptions have constraints outside of their control.

How else we can help

Most companies lose millions of dollars a year because they cannot hire or retain the data science talent necessary to advance their projects. We have helped some of the world’s largest and most successful companies build and retain the talent they need to achieve their business goals.

Want to improve your data science recruiting efforts? Get the most out of your team? Increase retention? Contact Russ Rands via LinkedIn or email him at russ@prolego.com.

Russ Rands
Chief Operating Officer + Co-Founder

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