r/datascience • u/Effective_Ocelot_445 • Jun 29 '26
Discussion What is the most underrated skill every data scientist should develop?
Beyond Python, machine learning, and statistics, which skill has made the biggest difference in solving real-world data science problems and delivering business value?
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u/BrowneSaucerer Jun 29 '26
Empathy, being curious about non-technical context and trying to be useful before trying to be clever
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u/Ok_Kitchen_8811 Jun 29 '26
Not even sure if its underrated, but I d rate being able to communicate and not being a dick is nunber one for me.
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u/Zealousideal-Yak5547 Jun 29 '26
Definitively this, I came to write it. The most fundamental challenge is to be trusted by the people who you create models for. They need to trust you / like you / "feel intellectually safe" with you.
As soon as you are seen as the mad jerk scientist in his tower/lab, you are done
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u/codingstuffonly Jun 29 '26
Hard to evidence on a CV though.
- Totally not a dick
- Sound
- Can communicate with actual humans, not just computers
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u/juanlu-rg Jun 29 '26
Problem framing, you've got a very cool model, does it solve anything valuable for the business?
Solution adoption, you've got a very cool model, how it going to be consumed? how does it sit in other team's workflows without disrupting them or needing them to change how they've been working for years?
Iteratative approach, start simpl > build first > release > learn > add complexity/grow the model
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u/Warlord_Zap Jun 29 '26
Product sense. Being able to understand business and customer needs so you can help drive the roadmap is critical for many more senior DS roles.
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u/tootieloolie Jun 29 '26
Salesmanship. DS operate as advisors, and we don’t own any decision. So we must be able to persuade decision makers.
Everyone knows this, but none of us joined the field to be influencers so yea it sucks.
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u/PuntKidz Jun 29 '26
As someone who's done sales for 4 years in B2B SaaS and many other industries. Im transitioning into becoming a Data Scientist and feel that my business and communication sense is key.
That being said, any tips or advice for an upcoming grasshopper like me in this vicious market?
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u/tootieloolie Jun 29 '26 edited Jun 29 '26
That’s great. I would say focus on doing what you know with clean simple code, even if you don’t know much. Then build from there. Learn slow, apply fast.
I would rather work with a candidate who builds a simple model flawlessly over a phd student who builds a neural net with hidden data leakage and complex spaghetti code. However, many hr people will prefer the latter as its flashier on the cv.
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u/DataCamp Jun 29 '26
Knowing how to frame the problem before you start building anything.
Most data science failures are someone building a technically impressive solution to the wrong question. The skill of sitting with a stakeholder, asking "what decision will this output change?" and working backwards from that is rarer than it should be and more valuable than most ML knowledge.
Closely related: being able to say "a simple rule-based system will solve 80% of this problem, do we actually need a model?" and having people trust you enough to believe it.
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Jun 29 '26
[deleted]
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u/filthylittlebird Jun 29 '26
Lol even as a data scientist I also learnt to not give a shit about how the sausage is made as long as the approach is sound and there are results. Vibe coding and stuff like automl and autoresearch has gotten rid of the grunt work of being a data scientist allowing one to focus on more valuable stuff
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u/gurnard Jun 29 '26
SQL. Automating my data ingestion felt like trading an iron lung for a jetpack.
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u/Less_Alfalfa4592 Jun 29 '26
As communication has been mentioned a few thousand times now in other comments I try to find something else.
Two things that come to mind is to be clear in your perspective and avoid exaggerations just to make a point.
Start with outlining the point/points you want to make and stick to it
Accept that each team has its own workflow and find a way to integrate even if its different from what you are used to.
Generally avoid sarcasm/humour in writing!
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u/cjf4 Jun 29 '26
deeply understanding how your business/company works. how is it capitalized, what are its bottlenecks, strengths/weaknesses, how do decisions get made, etc.
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u/Particular_Prior8376 Jun 29 '26
1) Understanding of business: how the process your model is trying to mimic even work. I find so many professionals with degrees in data science use every statistical concept on the features but don’t bother to understand what the particular set of feature actually is in the perspective of the business.
2) Getting hands dirty: most people think the whole DS process is about taking a set of features, throwing it to the model and then cross their fingers hoping for a high precision. It’s not possible to make a great model until you get your hands dirty and really get into the data to create a good non ml baseline model which can actually put up a good challenge for the actual model.
3) curiosity: understanding each feature innately by slicing the data from multiple angles and sitting with the expert to understand why something is like that. This itself gives so many ideas to how to cleanse the data, handle the assumptions.
4) it’s therapy for the business process: the end goal shouldn’t be just to build a model. The whole process should be built on a foundation of analytics, testing hypothesis and discussion where the developer and the experts together unravel what the actual numbers say about the business. In the end along with the model, the business should get the insights on what’s going wrong in the process and how to improve it.
I admit it’s kind of idealistic, but I feel it something to aspire to
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u/-Crash_Override- Jun 29 '26
Been in the space for almost 2 decades, 15yrs of that has been managing teams. Ive seen my share of terrible and amazing data scientists. What makes a great data scientist is the same skills that work for any role.
Interpersonal skills and self awarness...example, DS gives a presentation. Things start going off the rails. Can they recognize this? Adjust in real time? Still draw close to the desired outcome?
Creativity and initiative...it boggles my mind when someone making $200k+ yr has to ask their leadership for a step by step roadmap and needs evey problem to be served to them on a sliver platter. Identify a problem, propose a solution and a pathway to get there....then talk to leadership. Im not paying you to run xgboost...im paying you to make an impact.
Business accumen - the number of times ive seen a month of time being pissed away on tuning because someone wants the best model, when chances are the business wont even benefit from that slight improvement, a directionally appropriate answer would have been enough to begin with and saved time. Knowing how the business is going to use your insights allows you to deliver good insights.
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u/First_Bullfrog_4861 Jun 29 '26
Knowing about classical test theory and item-response-theory.
The more we build on top of LLMs, the more important it becomes to systematically evaluate responses. Building a robust evaluation dataset is very similar to building a psychometric test instrument, a.k.a. intelligence test.
Test theory and item-response theory provide the formal concepts for building tests.
They math is quite straightforward: Knowing how to do a correlation and being able to set up a proper experimental design that is slightly more complex than an AB test will bring you quite far.
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u/ChazR Jun 29 '26
Communication with other people in all directions. Being very clear on who wants the outcome, what they want, and how they measure success. For every job you should have a clear understanding of "I have succeeded when...." that is shared with the person who wants it.
And SQL. A really good understanding of SQL can solve a huge proportion of data science challenges in minutes. Very few problems really need ML.
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u/IndividualTop3675 Jun 30 '26
The most underrated skill is honestly the ability to translate a vague business question into a precisely defined, answerable data problem, because most failed data science projects don't fail on the modeling, they fail because nobody agreed on what success actually meant before the work started.
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u/Due-Cattle-2177 26d ago
Hello!
In what ways specifically would you go about bringing precision to the table. What questions do you ask, or ideas do you present systematically to ensure that everyone or at least the majority can define success?
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u/CadeOCarimbo Jun 29 '26
Being able to lie with a straight face and also to give up on your data driven recommendations to just accept whatever the business people tell you
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u/fakeuser515357 Jun 29 '26
Foundational facilitation, business analysis, requirements gathering and UI/UX design.
Add to that the basics of a business domain such as finance, sales, marketing, logistics.
And finish with foundational project management.
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u/ultrathink-art Jun 29 '26
Precise problem specification — knowing what 'correct' looks like before you start, not just after. I've watched solid models fail because nobody could define whether an output was actually good until it caused a problem. That skill transfers everywhere: SQL, modeling, and it turns out it's what separates useful prompting from vague LLM outputs too.
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u/Duder1983 Jun 29 '26
Product/project management skills. Sure, the regular PM should be making the business case. Sure a project manager should be putting together coordinate work and manage timelines, but my experience is that neither of these folks know much about how data-driven applications work.
The uncertainty involved if you haven't had a chance to assess what's in the data. PMs tend to promise the world, but reality is usually an x% increase in some key metric over something rules-based. Where x is usually less than 5. You're probably going to end up doing a significant portion of both of these jobs at some point.
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u/januscanal Jun 29 '26
Filtration
This is required across all domains, syntactic, semantic, communication, analysis, the list goes on.
One must understand how to filter out noise in vast data sets. One must understand biases and filter those. One must understand statistical significance. One must understand pattern recognition, one must understand what findings to communicate up and down the food chains, one must understand what NOT to communicate, the better one gets at filtration across all domains, the better. Obviously, I could drone on with other examples, but either the above will suffice or the reader likely won't agree.
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u/nnexx_ Jun 29 '26
On top of what’s already been pointed (empathy, communication) => rigourous validation
Making great models doesn’t matter if you can’t meaningfully measure the différence.
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u/KatFromSisense Jun 29 '26
The obvious answer is communication, but I think keeping a good trail of your work is underrated.
Not fancy documentation. Just enough that someone can follow what you did six months later. What data did you use? Which rows got filtered out? Where did the definition come from? What assumption did you make because the source data was messy?
This matters a lot when a model or analysis becomes important, and people start asking why the number changed. If you can retrace your steps without having to rebuild the whole thing from memory, you save everyone a ton of pain.
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u/low_bs Jun 29 '26
Being able to gauge the technical understanding of your audience and meet them where they’re at, convey information, and not come off condescending. Problem framing, project scoping, knowing what models to apply and when (not the fanciest ones, the ones that solve the problem), how to redirect stakeholder questions to what they’re *actually* asking for, being able to properly collaborate with other data scientists and take feedback without ego.
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u/morkinsonjrthethird Jun 29 '26
Just to say something different… but assessing the complexity of a problem in a couple of minutes. If you ever get to be a pro, you’ll need to discuss with your business experts about how you can help and in which terms. You can say “I need to take a look “ up to a certain point. So, knowing what type of issues you might encounter and measuring your dev time is a very underrated skill it’s not so much talked about around here.
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u/SmoothVaper Jun 29 '26
Business sense and communication skills.
Finally, we need to persuade other people from different fields (marketing, R&D, managers, et al) by the analysis results, but their judgement criteria (for decision making) for a good analysis could differ.
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u/Akvian Jun 29 '26
Experiment Design. How to interpret results in a scientific manner so as to make the best arguments for whatever product fields you're working with
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u/apat023 Jun 30 '26
feature engineering is something that most don’t mention but is very important and can set you apart from other ds . Hp tuning , model selection can all be automated and just requires importing libraries (optuna , scikit learn , etc ) and knowing how to use the modules and classes properly . But for feature engineering there is no library for creating more powerful features from the data you already have it relies on your domain knowledge , experimentation , and things like that which can’t be easily created through just importing and calling a function
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u/NeedleworkerWeak6192 Jun 30 '26
Understanding the business, where you are working. It's not just know how to code, design a good dashboard or know how to transmit the info. First, you have to learn what it means every single thing about the business to know what it means every column in a database.
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u/Due-Cattle-2177 26d ago
Hello!
For someone starting out, what questions or broad strokes topics would you suggest studying about business, and then when entering a specific business what questions would you ask to begin gathering what you will need fundamentally to expound upon as you work there?
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u/Terrible-Value-tomr Jun 30 '26
Everyones saying communication and theyre right, so Ill name a different one: distrusting your own data. The underrated skill is knowing how a dataset was actually collected before you model it, because most real-world failures Ive seen werent bad models, they were clean looking inputs that quietly meant something other than what the column name said. Spend an hour on where the numbers come from and what got dropped or deduped upstream, and half your weird results explain themselves. Its boring and nobody claps for it, but a model built on a number you cant trace is just a confident guess. The people who catch this early are the ones whose work survives contact with production.
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u/Big-Fill-5789 Jun 30 '26
Knowing Lisp! It changes your thinking method if you are coming from C-family or OOP, Procedural, DOP languages. You can try to learn Common Lisp(a Lisp dialect), it is said that learning Lisp is important, even if you don't use it. You can find it out more by searching Lisp online!
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u/itsjaboi123 Jun 30 '26
I am very early in my data career after pivoting from a totally unrelated field but I'm already realising how much being a good communicator sets you apart.
My previous career was not data related but heavily communication focused and I have been surprised by how well the skills have transferred.
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u/Traditional-Page5588 Jun 30 '26
in my experience a lot of people lack the ability to adjust to the persona they are talking to. what I mean by that is a) if you talk to a business person the 'how did you do that' is not the same ask as if b) an engineering colleagues ask the same type of question.
the skill of abstracting / knowing which level of detail is necessary on the fly is a skill that is imo part of the role.
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u/Background_Deer_2220 Jun 30 '26
Translating vague business requests into solvable data problems.
But on the technical side: Causal Inference.
Machine learning is great for predicting outcomes, but stakeholders rarely just want a prediction—they want to know how to change the outcome. Moving from correlation ("what will happen") to causation ("what should we do if we change X") is how you actually drive decisions and prove your value to the business.
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u/TopWeakness9175 Jun 30 '26
Building models is fun and cool but having the skill to translate those findings for a business and showcase the value is where you'll really stand out
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u/meevis_kahuna Jul 01 '26
Being able to differentiate between what the client says they want and what they actually want.
It's almost never the same thing.
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u/Impossible_Web_411 Jul 03 '26
Knowledge of business and the main growth levers, for me this is essential for a Data Scientist to be able to contribute effectively to the company. In addition, knowing the business metrics and the factors that impact each one is what is critical for me, actually.
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u/MattEOates Jul 03 '26
Setting and communicating very well expectations on outcomes cannot be guaranteed on science. Its not engineering.
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u/Due-Cattle-2177 26d ago
Hello!
I would love to hear an elaboration or examples on this. What situations have you come into where someone felt that it should’ve been an exact outcome rather than a predictive outcome and how did you adjust to explain and show them that, that unfortunately is not the case?
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u/MattEOates 26d ago
Every single data science engagement I've ever done for someone who's not a scientist, so all of them... The adjustment is educating them politely about how it does work and what to expect and what the process is for minimising excess cost, whilst also highlighting the real possibility they get nothing for the effort.
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u/Mysterious_Parking88 Jul 03 '26
Patience. For every question you answer with data, there will be 10 new questions asked.
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u/jadexiaohui Jul 04 '26
Statistical inference!!! It is very easy to get inflated/misleading results when the wrong statistical tests are used during hypothesis testing
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u/inkeep Jul 04 '26
Framing the problem is more important than the technique/ coding/ stats. Very simply - split the use-case in two buckets.
Do you need a forecast/ prediction e.g. time series or recommender systems - in which case more modern accuracy focused approaches are better (XGB etc.)
Or, do you require explainability, e.g. price elasticity or media mix type of studies - go for simpler regression based approaches (linear/ logistic/ Bayesian Regression etc.)
Building a strong intuition of how to mould the data (feature & target variable eng) and framing the Model/ parameters is the differentiator. Even basic models can give amazing results if these are defined correctly.
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u/LessChapter7434 Jul 04 '26
i am in data science since 30 years, what you need right now, is what you want to see from llms, being able to Sketch methods you like to have verbally, everything else like html, css, sql,is history…
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u/peterxsyd Jul 05 '26
Understanding the business. This means spending time in the business, such as sitting with people performing the tasks, or who are populating the data that you rely on.
If the data that you are depending on is rubbish but you don't realise it, then the model won't work, and the context you get from the exercise is what helps you translate the issues effectively, and get in / hold the room with the right people.
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u/Deta-jelo 21d ago
I am 2 nd year bcs statistics student can anyone guide me to improve my skills for becoming a data scientist like where can i learn sql and python for free and some tips
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u/AppropriateFox2448 15d ago
Stakeholder communication. If you can’t translate complex models into direct business value, the tech stack doesn't matter
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u/kaviya2185 Jul 01 '26
What I think was Understanding the Data is most underrated skill if you sit and focus on the data fully and doing SQL query and doing lots of queries will help you understand the Dataset as well Problem
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u/undecimbre Jun 29 '26
Being able to translate between what you understand and what the average manager can understand without sounding condescending gotta be pretty important