After interviewing with over 20 companies in the UK in one month Finally got an offer.
Background:
US undergrad + US master’s, 4 years of DS experience in the US. I don’t currently need visa sponsorship, but ideally the company should have the ability to sponsor in the future.
Roles applied for:
100+ applications in total, all submitted through LinkedIn.
Roles included DS / DE / MLE / AE. (Some interview records were tracked separately.)
Job search results:
One month is still a short time, and many processes are only at rounds 2–3. I’ve been rejected by some and just started with others. So far, only one offer, which I’ll most likely accept. I’ll finish the interviews already in progress but won’t take on new ones.
Here are a few observations from my job search:
1. Broaden your scope when applying.
Although I mainly targeted DS roles, I also applied to MLE/DE/AE because titles and job descriptions vary a lot across companies. To increase your chances, it’s worth trying multiple directions. It also helps you understand what other roles actually do. For example, I only learned about the Analytical Engineer role through interviewing.
2. Your body and attention reveal your true interests.
If you’re unsure which direction to pursue and are interviewing for different roles, notice your physical reactions and how much effort you naturally invest in preparation. You’ll subconsciously spend more time and energy on roles you truly care about.
3. Years of experience matter more than title.
Not a single company asked about my previous job title or level. They only cared about:
Business impact you created
Your core role in projects
Whether your skills match
I used to feel stuck about delayed promotions, but now I realize that was self-limiting (except for cases like FAANG where titles are more standardized in the market).
4. Job searching is truly a two-way selection.
Some things just can’t be forced. One company asked my view on compensation. I knew what they wanted to hear but couldn’t fake it—I said I value the work environment more.
I also met interviewers who focused entirely on textbook theory, like regression assumptions and p-values. Not that these are wrong to ask, but if that’s how they screen experienced candidates, it feels like they’re looking for good test-takers rather than people who can actually do the job.
5. A rant about data challenges.
In the age of AI tools, are they still necessary? Some requirements are honestly excessive. Expecting candidates to spend 7 days producing code + documentation + slides—do candidates’ time really not matter? 😂
Final thought:
I firmly believe that “those with good fortune don’t enter the wrong company.”
The opportunities you miss are just the universe filtering out what’s not right for you. The best fit is still ahead.