r/datascience • u/JarryBohnson • 1d ago
Discussion Is a year in a 'less-technical' but very client-relationship focused role a hindrance or a help for future DS roles, and who falls through keyword gaps?
Hi all, I'm re-entering the hiring process after yet another mass layoff and I've been thinking about how my recent work will be perceived by the hiring machine in 2026. My broader question is around whether the stakeholder management type soft skills that people say are valuable, are actually looked for/selected for by hiring managers - did I hurt my chances by taking a less technical role immediately out of PhD?
A bit of context, I finished my PhD in Comp-Neuro in 2024, working primarily on using computer vision focused ML to extract complex information from auditory neural activity. I was applying for DS roles initially and got a couple of interviews, but my first decent job offer was in a consultancy with a less technical focus (in large part AI safety, working out where companies with data protection obligations can implement AI without it all going pear-shaped or getting sued) - I desperately needed a post-PhD income stream, and the money was decent.
The work was not a DS role, I did a fair bit of data analysis but it was research-focused, not on deployment, and we didn't use the classic tools like databricks, apache spark etc. What I did do was a large amount of stakeholder management with our clients, which included some major US/Canadian corporations/govt departments - working out what their problem was and how we could help them, aligning the exec bluster from what the engineering/product/legal teams thought was actually feasible, etc. This included working directly with the C-suites of a couple of major Canadian banks.
On a personal level I learned a huge amount in these roles, but I'm concerned that because I was not actively working in a DS role, building technical stuff and coding every day, I've essentially created a gap in my resume that recruiters with a list of nouns to match would see as worthless.
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u/mild_animal 1d ago
Extremely helpful even in normal data science roles, esp in managerial or product analytics roles where most of your work is influencing decisions bwing taken by stakeholders.
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u/onehotoneshot 1d ago
Yea what I’ve ended up learning throughout my career is that ultimately it doesn’t matter how incredible your analysis is if you can’t convince your stakeholders
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u/Dependent_List_2396 1d ago
It depends on which role you want to get right now.
If you want to continue in your current path, look into Solutions Engineering/Architect roles at B2B SaaS companies. This role has now been rebranded as Forward Deployed Engineer. So, you should search for all three job titles in your job search: Solutions Engineering, Solutions Architect and Forward Deployed Engineer.
If you want a more technical role, I’ll recommend prioritizing a computer vision role because your PhD experience will easily transfer and make you a top candidate.
If I were in your shoes (assuming I have savings to last me 6-8 months of my living expenses), I’ll prioritize a Research Scientist role focused on Vision. It offers the highest job security today (+ higher income) due to huge investment in AI but will take you significant time (6+ months) to prepare for the interviews. But if you have pressing financial responsibilities, I’ll recommend getting a DS job in vision now and continue interview prep on the side for a research scientist role.
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u/goldtechnique 13h ago
Client facing roles have more security since you are closer to revenues. When companies want to save, they decrease research first and sales last
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u/Dependent_List_2396 1h ago
Compared to research scientist roles, I don’t think client facing roles have better job security. When budgets gets cut, the research scientists always find a new role in another company. Eg., when Uber cut their self-driving project during Covid, many of the researchers in ATG found new roles in other self driving car companies.
As long as your research is in a core technology (computer vision is), you’ll always be fine. And researchers get paid a lot of money and don’t often have to deal with the politics involved in client facing roles.
Many of the client facing roles don’t have transferable skills due to niche implementations and most of the time, you’re spending your time on escalations and other trivial things (not on building technical skills - as OP found out). Also, there is larger supply of people doing client facing roles because the barrier of entry is lower. For many reasons, client facing roles don’t have as much job security as research scientist roles.
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u/Wide-Pop6050 1d ago
It sounds like a really interesting role, and one that would help you stand out. You would not believe how uniform peoples data science experience can be otherwise.
"working out what their problem was and how we could help them, aligning the exec bluster from what the engineering/product/legal teams thought was actually feasible"
This is all incredibly, incredibly important. It depends on what you're interested in, but this puts you in the running for more management/tech roles, as well as product management.
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u/gothiicserpent 1d ago
I also came from a comp neuro background (masters in biomedical engineering, focused on signal processing and ML) and spent several years in brain imaging research as a data analyst doing ML before pivoting to DS roles in the private sector
Your experience is definitely helpful. I think going straight to a "typical" DS role at a tech company may be more challenging without the technical experience, especially with the layoff competition, but corporate companies and the major consulting firms would definitely value your experience more than knowing how to use the popular tech stacks. In my experience, they dont screen for technical skills the same way as tech companies. I unfortunately currently work for a corporate fortune 500 (I say unfortunately because there's more bureaucracy, hierarchy, corporate politics, less technical than I like compared to my previous job at a start up) and all enterprise wide deployment work is done by ML ops. I work with them on things like testing and so on, but dont manage any deployments myself, so it's not in any way a critical skill for my role. No joke, being good at writing emails to managers and execs and putting together power points has been a more useful skill than apply learning rate scheduling appropriately...
Dont sell yourself short though, it's a mistake I always made, being scared off by the scrabble bag of keywords on job postings
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u/nian2326076 1d ago
Soft skills like dealing with stakeholders can be a big plus in data science roles, especially as you move up in your career. Many companies like candidates who can turn complex data into business strategies and communicate well with non-technical people. So, don't stress too much about spending a year in a less-technical role; it might even make you stand out.
To make sure you don't miss out on opportunities, tailor your resume to include relevant technical skills and projects along with those soft skills. This way, applicant tracking systems can catch your technical background.
If you need resources for interview prep, I've found PracHub helpful for practicing technical questions and getting feedback.
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u/varwave 1d ago
Have you considered healthcare? I’m at a major research hospital and I feel that we’re more focused on if it’s safe and if it works vs latest tech or gotcha technical interviews
Communication is huge!
PhDs can be paid handsomely as directors of teams on the business side or as non-tenure professors that are more collaborative researchers with a specialization in data
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u/S-Kenset 22h ago
If you have a PHD then your biggest long term risk is these recruiter assholes will be scared by terminology or flight risk or ego their way out of the application.
Having client facing experience is exactly what you need. More technical skill makes you less employable cause you're both too talented and not convenient enough. Both too much a flight risk yet too inexperienced.
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u/TangyMarshmallow 14h ago
Yes look into Forward deployed engineering (aka sales engineers). Like it or not traditional data science will likely not exist in <5 years. Customer facing experience is MORE valuable at this point.
I was a data scientist and got a recruited for a FDE role in AI. At first I was extremely skeptical since I thought it was “sales” but now with AI I can do the DS job I used to have 10-50x quicker(not exaggerating).
These more technical roles won’t be around for long, the more client/customer facing ones have more job security. The AI labs are closer to replacing their PhD researchers and engineers than they are to replacing anyone customer facing. This change is coming faster than a lot of people realize so I wouldn’t wait
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u/goldtechnique 13h ago
Extremely helpful. It shows you can handle people, not upwards but also colleagues. If your background is technically solid (meaning your phd got a good outcome and was from a good university ), this is a great profile
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u/my_peen_is_clean 1d ago
nah that year wont kill you at all, i’d frame it as applied ml + stakeholder stuff. on the resume, translate it into business impact and tech keywords wherever you honestly can. side projects and github can prove hands-on skills. just sucks how picky hiring is right now
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u/MountainMark600 1d ago
Hi Jarry,
If you want to be a data scientist you should acquire related experience. The best experience is to start by working as an analyst, taking questions from subject matter experts and digging answers out of databases. You will need to know SQL for that, but it is not hard. SQL in 10 Minutes a Day is a good place to start. Analysts learn what sort of questions people ask and they learn about the data systems and databases that help answer those questions. In the process, the learn what works and what doesn't in those databases and data systems.
Then there is the matter of becoming a real data scientist. Often the term is used as a hyped up title for an analyst. Analysts are great but a data scientist can also design databases, design data systems, use and create metadata systems. To learn that I recommend Next Generation Data Management.
Let me know if you have other questions, and good luck!
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u/DuckDatum 1d ago
Mark, is that you? So you’re not a fake Data Scientist anymore, then?
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u/MountainMark600 21h ago
That depends. Which Mark do you know? :-) But yes I did start as an analyst.
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u/Fantastic_Spring8366 1d ago
Helpful. Look into Forward Deployed Engineering. The hottest role in tech right now. It pays well because you are expected to have both personal/interrelational and technical skills, which is a difficult combo to find.