avatarPraveen Pareek

Summary

The article addresses common questions and concerns among data scientists in the industry, emphasizing the value of mediocre professionals and providing guidance on career growth and job selection criteria.

Abstract

The piece titled "Top 5 General Questions by Data Scientists in Industry" delves into the introspections and aspirations of data scientists who may not have the most impressive credentials but are integral to the industry. It offers a toast to the 'mediocre' data scientists, acknowledging their contributions and highlighting the importance of job satisfaction and quality of life over traditional markers of success like working for FAANG companies or winning Kaggle competitions. The author encourages readers to reflect on their personal growth, the influence of their work on the company's success, and the factors that contribute to a fulfilling career, such as leadership, reporting structure, and the critical nature of their team's role. The article also provides a framework for evaluating job opportunities based on personal criteria and the potential for professional development.

Opinions

  • The author believes that mediocre data scientists, who may not have high-profile achievements, deserve recognition for their steady contributions to the field.
  • Quality of life is posited as a crucial metric for job satisfaction, potentially outweighing the prestige of the employer or the intensity of work.
  • A fulfilling job is suggested to be a balance of fun, proximity to home, learning opportunities, growth potential, respectful coworkers, supportive management, and adequate compensation.
  • With experience and a hot job market, data scientists are encouraged to be selective and thoughtful about their career moves, considering the leadership, organizational structure, and the strategic importance of their role within the company.
  • The article suggests that data professionals should aim to work in organizations where data science is given a high priority and is closely linked to the company's core products or revenue streams.

Top 5 General Questions by Data Scientists in Industry

Photo by Boitumelo Phetla on Unsplash

Are you working in an industry?

Are you working in a role which demands the general awareness about data?

Are you curious about the industry trends?

Are you curious about your growth?

Do you remember to stop every once in a while and think about how far you’ve come?

Do you want a Shout Out for all the Mediocre Data Scientists Out There?

If your thoughts match any 2 or more of the above questions, then you should keep reading till end.

So, let’s start from the last question.

Shout Out to All the Mediocre Data Scientists Out There

I’ve been thinking about it for a while now and all too often I observe people claiming they feel inadequate and then they go on to describe their stupid impressive background and experience.

That’s great and all but I’d like to move the spotlight to the rest of us for just a minute.

Photo by Emerson Vieira on Unsplash

Cheers to my fellow mediocre data scientists who don’t work at FAANG companies, aren’t pursing a PhD, don’t publish papers, haven’t won Kaggle competitions, and don’t spend every waking hour improving their portfolio.

FAANG is an acronym referring to the stocks of the five most popular and best-performing American technology companies: Facebook, Amazon, Apple, Netflix and Alphabet (formerly known as Google)

Even though we’re nothing special, we still deserve some appreciation every once in a while.

It’s okay for your job to just be a job. However, it’s even better, when considering quality of life as main metric.

I could imagine that a fulfilling job can improve the quality of life quite significantly. On the other hand having a diverse life, not contingent solely on the job is probably more robust.

So, how are you measuring the “quality of life”? I’ve found that a good equation involves

  • How much fun it is
  • Distance from home (in time)
  • What can you learn
  • How can you grow (Also the organization in which you work, as your growth also depends on that)
  • How much they pay you (And how much value you add to the organization)

Find your equation with these variables, compute your betas, rank different jobs.

You’ll be surprised. Rule is that first you rank those 5, then, in the equation, weights cannot change that rank. (I put them in my personal order)

Two other major points I think we can add here:

  • Enjoying and respecting your coworkers
  • Management that is supportive and professional

Now, enough about the shout out!!!

Are you curious about your growth?

What are you looking for in a new job after having a few years experience and being in high demand?

Suppose you are a data engineer with 2 years experience at a big company and a master’s degree.

After finishing your degree, you were unemployed, pretty much were open to any work, submitting 100s of applications, and talked to every recruiter that slipped into your inbox.

Now after a few years of experience, having a stable Full Time job, and entering a hot market, it is weird for you getting call backs a few days after submitting an application, being able to turn down interest or interviews, or evening naming a higher salary requirement.

Overall, you’re seeing this passive job search as having the opportunity to choose and think about what you’d like your next career step to be.

Obviously this is an extremely lucky spot to be in.

But having all the choice does make it a bit more difficult to navigate — especially when sifting through Full Time, contract-to-hire, contractor positions, as well as start-ups, FAANGS and other tech companies, and big companies just starting their data science programs.

So, here are the things in brief (What you can go for):

  • Leadership is a big one.

The more senior you become, the more you become exposed to the quirks of the people actually running the ship. Are these people you respect? Have they done this before? Can you imagine yourself working with them?

  • Who do you report to, and who’s the highest ranking person in your function?

If you report to an entry level or middle data science/engineering manager but he’s reporting to some random person in finance, or IT, or marketing, then it’s not a great sign for you. you’d like to see the top data guy only a layer or two removed from the CEO; means you’re more likely to have a seat at the table where decisions are made. A standalone data organization is great; rolling up to Engineering is the next best.

  • How critical is your team to the success of the company?

In most organizations, data is a support function. Nothing wrong with this per se, but the closer you are to the product or revenue generation, the more influence you’ll wield as a group. This reduces the probability of getting jerked around by leadership, which happens a lot to data professionals.

End of the day, these are my summary statistics to estimate how much leverage you have, and how many obstacles you’ll face in doing your job.

What’s next for you?

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