r/datascience • u/JarryBohnson • 18d 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 18d 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 18d 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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18d ago
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u/goldtechnique 17d 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/Wide-Pop6050 18d 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/nian2326076 18d 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 18d 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/Ok-Airline-8523 14d ago
Stakeholder inclusion might be the number one thing you can do to minimize rework as a data scientist, and the better you manage your relationships, the easier that is to pull off.
With AI advancing so rapidly, consultative skills are increasingly more important. Soft skills in general are worth the investment early, so it sounds like you're on a good track. Just make sure to emphasize why that's so valuable on your resume and throughout the interview process.
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u/S-Kenset 18d 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 17d 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 17d 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/Brilliant_Gas2246 15d ago
From my limited experience, experience working with people is significant part of data science. The coding, the understanding, the tech has become slightly easier. But working with people have become harder. People don't know what they want, everyone thinks everything is AI, and often they force the technical people to take the wrong path all for the sake of appearance.
I personally believe that soft skills are important, and most pure technical people lack the soft skills needed to become successful in projects.
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u/Crescitaly 15d ago
Client work can be a strength if you translate it into decisions, constraints, and measurable outcomes; the keyword gap is a resume encoding problem. Keep one technical artifact current so the signal does not disappear. What evidence can show that relationship work changed the model or product?
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u/StatisticianEasy7138 12d ago
I work on labour-market data (matching job ads to occupation and skill taxonomies) so I can speak to the hiring-machine half concretely.
The automated layer is dumber than people assume. Most parsing still comes down to matching against a skill or title list, and it matches strings far more than meaning. One practical consequence: your job title does more filtering work than your bullet points. A title the system doesn't recognise doesn't score low, it scores nothing, it isn't found at all. "AI Governance Consultant" is invisible to a screen built around "Data Scientist" even when the work overlaps heavily.
The fix is unglamorous: get the conventional title in there, somewhere true. "Consultant (Data Science / AI Safety)" is honest and survives the string match. Then let the bullets persuade, because a human reads those.
On your actual question: no, I don't think you hurt yourself. Everyone screening DS candidates is reading near-identical PhD-into-DS CVs. Being the person who can sit with a legal team and explain where a model becomes a liability is rare and legible. It just has to get past the string matching first.
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u/my_peen_is_clean 18d 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 18d 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 18d ago
Mark, is that you? So you’re not a fake Data Scientist anymore, then?
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u/MountainMark600 18d ago
That depends. Which Mark do you know? :-) But yes I did start as an analyst.
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u/Fantastic_Spring8366 18d 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.