Visualizing My Data Science Job Search (2024)

Reflections from a humbling journey trying to find a job in 2023

2023 was a turbulent year for many job seekers. At least for me, it felt like quite a journey. Over the 11 months between January and November, I had 107 career-related conversations and applied to 80 positions, resulting in 2 offers (I took a “break” in May to defend my PhD 😅). Some applications were stretches, sure, but many of them were posts to which I thought I’d be a great match — or at least worth a conversation!

The frequency with which my efforts were met with silence surprised me. I felt like most of my cover letters were quite earnest and my resume had unique experiences, prompting me to second guess my communication skills. Is my resume not clear? Cover letter too wordy? Am I misrepresenting my skills? Am I not actually good enough for any of these positions?? It was certainly a disheartening time — a feast for the impostor syndrome goblins lurking in the corners of my mind.

From a learning perspective, it’s rough that rejections tend to come without any sort of feedback. Which parts can I improve for my next application to a job for which I think I am definitely a strong candidate? Many kind souls offered helpful suggestions on my materials, which I do think got better over time, but it’s hard to say what actually made a difference in the end. Maybe I finally started submitting strong/competitive applications after months of weaker attempts? Maybe 2023 was a particularly rough year? Maybe I got unlucky with factors out of my control, like hitting fake job ads simply posted to “drum up interest” in a company, or ones pre-destined for an internal hire?

In the spirit of sharing potentially helpful nuggets with others on the job hunt (and ok, maybe an attempt at catharsis through visualizing an experience that frequently activated my tear ducts) here are some data-supported reflections from my 2023 job search process. I was most eagerly searching for positions at the intersection of Biology x Data Science x Climate Solutions, either in Seattle or remote.

For my 80 job applications, what route did they take from submission to a final “yes” or “no”? Figure 1 is a Sankey chart, designed to show the flow of volumes between nodes.

In the second row of Figure 1, we can see that of those 80, about half were positions to which I uploaded my info to a company’s portal without knowing anyone there, which I call a “Cold Application.” For the other half, I had a “Personal Contact” — sometimes this was someone who knew me well and could refer me, but often it was someone who I had met once (for coffee, a zoom chat, at a networking event). They didn’t necessarily know my specific skills super well, but they knew I was a real human, hopefully thought I was friendly/thoughtful, and could potentially relay a message to another real human to at least look for my application in the pile.

Visualizing My Data Science Job Search (3)

The third row shows how many applications resulted in an interview request or some form of rejection/no answer. Some folks insist that you should never apply to a job where you don’t already know someone internally that can refer you — it’s just a waste of time.

My data support that having a personal contact improved my rate of interview requests: 11/42 Personal Contact applications led to a conversation for a ~25% interview rate, compared to 4/38 Cold Applications for a ~10% interview rate. So about a quarter (4/15) of my interview requests came from a Cold Application. I would not go as far to say never apply to a position without a personal contact — it can be fruitful for the right position — but definitely put effort into making personal contacts. At least for me, this application path was more successful in reaching a real human with decision making power.

Some companies are diligent enough to send you an automated rejection when they have moved your application to the “No” pile (n=21). Very rarely I had a human tell me “no” from just the application stage (n=3). Just over half of my applications were “Lost to the Abyss” (n=41), where I heard back from neither human nor robot, and can only assume some void-dwelling AIs are munching on my cover letters.

In the cases where a human requested an interview, I usually proceeded to an initial screen, either by HR or the hiring manager. If that went well, I met more folks from the wider team, and in 3 cases I was invited to give a job talk about my research. None of my interviews included formal coding challenges — most of the positions I applied to had more of a science/research focus over software/infrastructure — but I was often asked for links to my personal github account.

In the bottom row, we see the final tally. I got 74 “No’s” before 2 companies finally said “yes” (within a day of each other, which was some wildly good luck after being on the hunt for 11 months!). I withdrew my other in-progress applications after I accepted my current job.

A big note: applications were not my first steps on my job search. After setting my PhD defense date for Spring 2023, I started setting up “informational interviews” with folks starting in late 2022. Many early conversations were with closer colleagues/mentors/peers whom I felt comfortable asking for advice and trusted with my detailed career goals. This helped me 1) feel better starting a process I was anxious about, and 2) communicate that I was officially ~on the hunt~ so people could let me know if any interesting positions crossed their radar in the coming months. Definitely tug on your support network as you venture into this process!

Next, I started setting up meetings with new people — either through a warm intro from a mutual connection or just an out-of-the-blue message on LinkedIn (some cold-LinkedIn messages got no response, but some did! Maybe ~half?). These requests were generally framed as “I’m a soon-to-be-graduating PhD student exploring career routes in the [Computational Biology/Data Science/Climate] industry — may I ask you about your experience working at [your current position], your career path that led you there, and any advice you have for someone early on this journey?” I also briefly personalized each message so it was clear I was interested in speaking to this person specifically and wasn’t just spamming everyone at that company.

Visualizing My Data Science Job Search (4)

The top panel of Figure 2 shows I kept up a relatively steady rate of reaching out to people. Even though most conversations were not about an active job opening, they did not feel like a waste of time. Some key benefits of informational interviews:

  1. Since I wasn’t immediately looking for a job (but would be soon), the casual framing of “just chatting over coffee” or “just learning about your experience” removed a ton of pressure. It was easier to be myself and get practice explaining my interests and experiences out loud without constantly fearing that I wasn’t saying things optimally enough to get the job. It was also great to practice active listening and asking questions to dig deeper into technical/science details with smart people.
  2. It was helpful to keep a pulse on the industry — people could tell me more candidly if their company was not likely to be hiring in the near future, or if they were somewhat likely to have openings soon. This helped focus my attention more directly to certain company’s career pages while attending more casually to others. Also, now that they knew me, these new contacts could possibly let me know if their company’s hiring status changed in the future.
  3. Just meeting people friendly enough to have a chat/coffee with a stranger was pretty cool. Sure some chats were a bit awkward, and occasionally I could tell the other person kind of wanted to leave, but most folks were very encouraging/supportive and were happy to elaborate about their own journeys. Meeting folks working on cool things kept me inspired, and often they’d suggest another person they thought would have a useful perspective for me, so the conversation train continued.
  4. After chatting with someone, if I later came across a relevant job post at their company, I would always let them know that I applied! They didn’t know me well enough to deeply vouch for my skills after one conversation, but hopefully they at least had a positive impression of me. If they felt like it, they might mention my application to a real human, who then might actually read my cover letter rather than leaving it stuck in the AI filters 🙃

The second panel in Figure 2 shows job application events: I submitted a sprinkling of applications in early 2023, paused right around my defense date in May 😮‍💨 and then ramped up late summer. The third panel shows when (finally!) a trickle of interview requests started coming through!

In one rather strange experience, I got an offer from a company, attempted to politely negotiate, and then swiftly received an “actually, we changed our mind, no more offer” email. This was pretty jarring. It was the first time I had ever tried negotiating, and as a woman looking for a computational job, it has been hammered into my brain that even if it feels unnatural/awkward/uncomfortable, you just gotta try! I had assumed the worst that would happen was they would say “no, that’s really our maximum offer, take it or leave it.” I’m pretty sure I was not being rude or unreasonable in my request, but having an offer retracted was quite upsetting 😖 I feared I had committed some big faux pas in trying to advocate for myself and fair compensation. Several mentors assured me that this was a highly unusual outcome and not a reflection on me, but of something weird happening at that company. I hope it does not happen regularly to others, but it’s not impossible (n=1).

The bottom panel shows that in November, I finally received two real job offers, thus concluding the Great Hunt.

To put this job search timeline in the context of the wider economic climate, I grouped each interaction type — casual informational convo (dark blue), job application (turquoise), and formal interview (green) — and plotted a cumulative count over time, overlaid with the trend of the S&P 500 index (purple) (Figure 3).

Visualizing My Data Science Job Search (5)

Other than the gap in May around my PhD defense, my informational convo rate was pretty steady over the year, while my job application rate increased in late summer, eventually catching up to my informational convo total. I’m not practiced in reading stock market trends and variability, but if I squint at it, it seems like an S&P 500 upswing after some lows in late 2022 roughly coincides with the steeper slope of my job applications (perhaps when more positions were being posted?) and a corresponding uptick in interviews 👀 Notably, this came after a series of big tech layoffs (>10,000 people) in early 2023 near some S&P 500 local minima.

In grad school, I knew my “Science Happy Place” was to work on a project where I was:

  1. thinking about biology (because genetics is just the coolest!)
  2. doing computer science (because I enjoy the puzzle solving nature of programming work)
  3. with applications in sustainability (because climate change is devastatingly urgent to solve)

Excitingly, I found my way into a grad school lab where I was using computational methods to analyze genetic data in methane-eating bacteria. I hoped my career would similarly encompass the intersection of my knowledge, skills, and enthusiasm in these 3 pillars of Science Happiness.

There were several climate-minded synthetic biology companies I had set my sights on, but in early 2023, I learned that nearly all of the ones with a computing team were in hiring freezes 🥶 Maybe hitting all 3 of these Science Happiness pillars was too much to expect in a fragile economic climate and I’d have to accept a job with only 1 or 2, especially if I was intent on living in Seattle?

My job search energy shifted in focus throughout the year, indicated by the density of colored circles back in Figure 2. Conversations, applications, and interviews are colored by company focus (as a computer science student, most positions I applied to had at least some computational element, and the broader companies were generally tackling problems related to biology, climate, or a combination). Though working towards climate solutions is where my heart is, I initially convinced myself that I would be ok working on something not-climate related while building experience and skills. Accordingly, in early 2023, we see I focused more on conversations and applications in “Biology + Data Science” (blue circles, typically health/pharma applications) when “Biology + Climate + Data Science” positions were quite sparse.

A few existential crises later, my thinking flipped: I decided I’d rather work on a climate application, even if it meant my job would not leverage my years of study in genetics/microbiology. Later in 2023, my job application effort reflects this mindset shift to “Climate + Data Science” (orange circles, climate work without biology). While I was initially nervous to pursue positions further outside my biology wheelhouse, I enjoyed the chance to learn about adjacent fields and consider how to adapt my skills to the types of non-biology climate solutions companies were tackling.

Figure 4 roughly summarizes the total energy I dedicated to each job field within the intersection of biology, climate, and data science, where each informational convo, job application, and formal interview counts as an energy unit. Notably, I didn’t get to pick when I got formal interviews — that depended on someone else choosing to talk to me — but I decided to sum these three energy units together as ~instances of intense focus/stress.~ This sum does not capture other types of effort, like reading, research, and preparation, but those tended to be more spread out over time.

Visualizing My Data Science Job Search (6)

Luckily for me, Fall 2023 brought about a thaw to some previous hiring freezes and a few super exciting positions opened at companies that combined computing and biology for climate applications (green circles in Figure 2, and dark green center circle in Figure 4). THESE were the exact kinds of positions I was hoping to find because they so strongly aligned with my interests, skills, and values! I eagerly messaged all the folks with whom I had previously made connections at these companies and expressed my most earnest enthusiasm about my applications!

At long last, I landed one of those jobs at the perfect intersection of my career goals: I’m in my early days as a data scientist at LanzaTech, a biotech company that engineers bacteria that eat carbon emissions from industrial waste streams and convert it into sustainable materials 🤓

In summary, I’m not totally sure which aspects of my job search led to my applications being received particularly well or poorly. It felt long and stressful, and the lack of feedback from the big pool of Automated Rejections and applications Lost to the Abyss was hard to train on.

Speculating a bit, my biggest piece of advice to pass on is to talk to people! Often! We all hear about the importance of ~networking~ (building out your professional contacts, etc). It can feel awkward and exhausting to initiate conversations with strangers all the time. But coming to each conversation with an earnest intent to connect, listen, and ask questions can be super helpful! Informational interviews are low pressure settings in which to form connections and share your interests.

Not every conversation will directly feed into a job opportunity, so it can seem like a lot of time to invest without a direct result. But you never know which ones may eventually open a door. An anecdote in my case: I happened to sit at the same lunch table with someone from LanzaTech at a conference in 2016 and kept up a casual conversation with that person via email (once every 1–3 years?). In late 2023 (7 years later!), a job posting opened that matched my skillset, so I messaged this person (and a couple other folks whom I had met from LanzaTech recently). I told them how genuinely excited I was about the position, which perhaps helped flag my application to real humans in hiring positions to continue the conversation :)

If “professional networking” feels intimidating, maybe think of your connections as a garden — not every seed will yield a fruit, and some things grow fast while others grow slow. Gardens don’t always flourish exactly when you want them to (like when your grad school health insurance runs out and you really would like a tomato to magically appear and offer you a new medical plan). But gardens make healthy, steady progress when periodically tended, and are generally a lovely breath of fresh air when you need support.

It’s not too late to start a career connection garden — it can be as simple as an earnest-but-professional message to someone that seems interesting 🌱

Best of luck out there, job seekers 💚

Thanks to Daniel and Matt for feedback on my early drafts!

Visualizing My Data Science Job Search (2024)


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As a data science fresher, you can start your career as a data visualisation specialist in industries such as e-commerce, finance, healthcare, and consulting. The average salary for a Data Visualization Specialist is 10-12 lakhs per annum. Data scientists are in great demand.

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Inadequate Resume and Cover Letter:

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Your mathematics and statistics skills need to be practiced.
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1 more row

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With an understanding of how long it takes to learn data science, it all boils down to your familiarity with some of the topics listed above. If you do possess a basic understanding of relevant concepts, you can start your glorious career to become a data scientist in just 6-8 months.

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Data scientist jobs are predicted to experience 36 percent growth between 2021 and 2031, according to the US Bureau of Labor Statistics [2]. Operations research analyst (or data analyst) jobs are projected to grow 23 percent, another high-growth job title [3].

Is it hard to get a data science job in the US? ›

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