Data Science Job Hunt: the key to finding a job faster

Hey friends!

The other day, while I was at a networking event, I found myself in a sea of shared experiences with other women in Tech.

The most common thread was the current job market and the challenges we could face finding a job.

They bravely shared their feelings, the angst, and the loss of faith in finding a fulfilling job.

I even heard a comment saying how looking for a job feels like a full-time job!

And if you are looking for a job at the moment, you might share the same feelings.

First of all, let me be very clear that all of those feelings are absolutely normal. There is nothing wrong with losing faith or feeling like finding a job is like a huge mountain for which you’re far from being prepared to start climbing.

In moments of uncertainty, it is human to feel fear and feel like we don’t know what we are doing, especially if we’re switching careers.

That’s why I would like to offer you a different perspective that could help you in your present or future job search in Data Science.

What would I do if I were looking for a Data science-related job?

In short, I would expand my job search to cover many other possible roles.

Remember that inside data science, there are a lot of different roles and a variety of skills.

So please don’t get married to the usual roles like data scientist, data analyst, or business intelligence.

So, what to do instead?

How to expand your options in data science?

The first step is to know what I bring to the table, or in short, my skills. What combination of skills do I have? What am I good at? Let’s start by taking inventory of my skills.

Skills Inventory

I suggest taking a few minutes and listing your current skills.

To give you an example, my (non-extensive) list would be something like:

  • Data Visualization (pyplot, matplotlib, bokeh, etc)
  • Data analysis (understand, synthesize, and extract conclusions from large quantities of data)
  • Programming languages (Python, Mathematics, a little bit of R and Matlab)
  • Predictive modeling
  • NLP (tagging, summarization, sentiment analysis, NER, etc.)
  • Research (identify sources of information applicable to a given problem. Define a problem, identify possible causes, and find appropriate solutions.)
  • Technical writing (publication of articles in peer-reviewed magazines)
  • Science communication (invited talks, blogs, local paper interviews, TED-Ed.)
  • Problem-solving (define a problem and identify possible causes, comprehend large amounts of information)
  • Probability and Statistics
  • Plasma physics
  • Ultra-cold atomic physics and Rydberg atoms
  • Quantum mechanics and quantum computing
  • Team management (directed the research work of others, assigned tasks, provided motivation, teaching, and mentoring)
  • Manage projects from beginning to end.

Think, for example, if you have mentored peers or taught skills to others. Or think about the moments when you collaborated on projects. Everything count.

*You can see I also added my skills from my physics-related work. Why? Because all my skills are important! Sometimes, one apparent unrelated skill is super useful and applicable to another field. But let’s go back to the exercise.

Now, here comes the good part.

Data Science or AI?

Some time ago, I wondered how exactly Data Science and AI are related. Are they basically the same thing, or something completely different?

Let’s see what others say about this question:

For example, this is how IBM defines Data Science:

“Data science combines math and statistics, specialized programming, advanced analytics, artificial intelligence (AI) and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization’s data. These insights can be used to guide decision-making and strategic planning.” Source.


And this is how Amazon answers the question:

“Data science jobs include data scientist, data analyst, data engineer, machine learning engineer, research scientist, data visualization specialist, field-specific analyst roles, and more. AI also encompasses all of these roles. However, as the scope of the field is so broad, there are many additional associated roles and areas of job focus…Depending on the role within AI, the skillset required may be more technical or soft skills-based. In some roles, there may be no technical experience required.” Read in full here.


Did you notice how both definitions point to the main idea that AI is present in Data Science?

Did you notice how many skills I already have would allow me to apply to many other AI-related roles beyond Data Scientist and Data Analyst?

These are just two examples from big companies, but I think they help to give you a better idea of what is happening in the DS vs AI world.

The main point is:

If I were looking for a job with my current skill set, I would definitely aim for many more roles than the typical Data Scientist or Data Analyst roles.

Why should I aim to get a job as a data scientist when there is a spike in AI roles that also use my current skill set?

So, if you are looking for a job, focus on leveraging your skills and expand your search to include roles in AI.

The world is always changing and continuous adaptation is part of the game.

And remember that new roles are emerging every day because of the rapid development of AI and AI tools.

I wish you the best in your job search!

Are you looking for a job atm? What are your paint points?

Let me know in the comments.

Have a great week!

Lina Marieth xx

Talks I’m attending

Last week, I attended the talk “NLP projects with spaCy” – by Vincent Warmerdam, organized by the community of Pyladies-Amsterdam. The workshop ended up being an easy-to-follow and very understandable introduction to the world of NLP with Spacy. Watch it if you are wondering what all the fuss is about NLP and some of the things you can do with it.

Quote of the week

Comparison can be the thief of peace and contentment, or it can motivate you to seek your own version of joy.

 Nedra Glover Tawwab

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