What Soft Skills Do Data Analysts and Data Scientists Need?
Discover the soft skills Data Analysts and Data Scientists need to succeed, from communication to problem-solving, plus how to build them for your data career.
A lot of people imagine data roles as purely technical, but the reality is very different. Whenever someone comes to us asking how to get started in data analytics, the conversation naturally shifts from tools and certificates to the way they solve problems, communicate ideas, and work with others.
From my own experience supporting career-changers, the people who stand out aren’t just good with Python or SQL. They’re the ones who can explain a messy dataset in plain language, ask the right questions, and adapt when a project suddenly changes direction.
In this guide, I’ll be walking you through the soft skills that matter most in data analyst and data science roles today. I'll also explain how you can start building them, ready for your next job.
More generally, soft skills shape how effectively you use the technical knowledge you’ve worked so hard to build. If you want a quick run-through, check out our what are soft skills guide.
Written by
Sophi Barnes is an experienced Careers Services Consultant with a strong background in career coaching and deep expertise in the tech and project management job markets. She's passionate about supporting people through career transitions and into roles where they can thrive.
Why Soft Skills Matter in Data Roles
I’ve met plenty of people who can write a tidy SQL query or build a dashboard, but the ones who really make an impact bring something more. Technical skills get you in the door, but soft skills determine what you can actually do once you’re there.
The difference between someone who’s technically capable and someone who’s genuinely high-impact usually comes down to how they think, how they communicate, and how they handle uncertainty.
With AI taking care of more repetitive tasks, the human parts of the job have become even more important. You still need tools like Python, SQL, or Power BI, but you also need the judgement to decide what questions are worth answering, the communication skills to explain your findings clearly, and the confidence to challenge assumptions when the data points in a different direction.
People often get a clearer sense of what the role involves once they’ve looked into how to become a Data Analyst, because it highlights how much of the job depends on judgement and communication, not just tools.
My view: From what I’ve seen, soft skills aren’t just good things to have. They’re what turn analysis into outcomes. And they’re often the difference between staying at entry level and stepping into more senior work.
62% of UK Organisations Say They Can’t Meet Their Goals Due to Skills Gaps
A UK Parliament briefing found that 62% of AI-focused organisations lack the data and analytical skills needed to meet their objectives.
The gap isn’t just technical: employers specifically highlighted shortages in communication, problem-solving and the ability to interpret data for non-technical teams. This mix of missing soft and hard skills is now one of the biggest barriers to AI adoption in the UK.
Essential Soft Skills for Data Analysts and Data Scientists
Now I want to share the soft skills I see making the biggest difference for people stepping into data careers. These are the skills that help you move from simply “running the numbers” to actually influencing decisions and being seen as someone the business relies on.
1. Analytical Thinking Beyond the Tools
I’ve seen people get so focused on learning the “right” tools that they forget the real job: thinking. Analytical thinking is about framing problems properly, questioning assumptions, and understanding the bigger picture before you ever open a notebook or dashboard.
Good analysts don’t just run queries; they ask why something matters and what decision it supports. Even with AI speeding up technical tasks, you still need the human ability to interpret context, challenge a brief, and spot when something simply doesn’t add up.
2. Communication: Turning Complexity Into Clarity
One of the quickest ways to stand out in data is being able to explain things simply. I’ve worked with learners who can build impressive models but struggle to talk about them in plain language. The truth is, most of your audience won’t be technical. They want clear insights, not jargon.
Being able to tell a story with data, like what happened, why it matters, and what you recommend, is what makes your work usable. This often becomes the skill that moves someone from “good” to genuinely trusted.
3. Curiosity and a Learning Mindset
Curiosity is an underrated superpower in data roles. The people who do well long term are the ones who keep asking questions, even when the brief looks straightforward. In a field that evolves constantly, you need the willingness to explore new methods, test ideas, and keep learning.
I see this a lot with career-changers: the ones who treat learning as part of the job, not a one-off task, make faster progress.
4. Collaboration and Stakeholder Management
Data work is rarely done alone. You’ll be speaking with Product Managers, Marketing teams, Engineers, and leaders who all need different things from the same dataset. Being able to understand their priorities, ask good questions, and manage expectations is essential.
I’ve watched people with strong stakeholder skills move ahead faster than technically stronger colleagues because they communicate early, handle disagreements calmly, and can balance different needs without losing sight of the goal.
5. Attention to Detail and Quality Judgment
Accuracy matters in data. A single typo in a query or a misinterpreted variable can send a project off in the wrong direction. But it’s not just about obsessing over small details. It’s also knowing when something is “good enough” for the decision at hand. I often help learners find this balance.
Employers want people who can produce reliable work without getting stuck perfecting tiny things that don’t change the outcome. It’s a judgment you develop over time, but it starts with building good habits.
6. Ethical Decision-Making and Data Responsibility
Working with data means handling real people’s information, even if it doesn’t feel personal on screen. Ethical judgment is becoming a core skill, especially with tougher expectations around privacy, fairness, and transparency.
I always remind learners that just because you can analyse something doesn’t always mean you should. Understanding bias, using data responsibly, and thinking about the impact of your work builds trust. And trust is something every good Data Analyst and Data Scientist needs if they want to progress.
Over Half of UK Employers Will Prioritise Soft Skills Over Hard Skills When Hiring
A 2025 UK workforce trends report shows that 64% of employers plan to prioritise soft skills such as communication, adaptability and collaboration during hiring. Even in technical areas like data and analytics, employers said that strong interpersonal skills often determine how quickly someone can progress.
The report also notes that automation is amplifying the value of human judgement, making soft skills increasingly central to recruitment decisions.
Role-Specific Soft Skills: Analyst vs Scientist
I’m often asked whether Data Analysts and Data Scientists need different soft skills, and the answer is yes. Sort of. But there’s a lot of overlap.
Both roles rely on clear communication, good judgement, and the ability to work with people who don’t speak in technical terms. Where they differ is in how those skills show up day to day.
- When I’m supporting learners moving into Data Analyst roles, I usually emphasise business awareness. Analysts spend more time understanding priorities, translating questions from stakeholders, and deciding what matters most right now. Strong communication and smart prioritisation make a huge difference here.
- For Data Scientists, the skill set leans more towards experimentation. You’re testing ideas, dealing with uncertainty, and accepting that a lot of what you try won’t work the first time. The people who thrive in these roles are resilient and comfortable exploring different approaches without getting discouraged.
Anyone exploring how to become a Data Scientist will notice how often adaptability, perseverance, and clear communication come up alongside the technical skills.
In short, both roles rely on the same foundation, but each one asks you to flex it in slightly different ways.
How to Build These Skills
When someone is just starting out, they will ask me whether soft skills can really be developed or if they’re something you “either have or don’t.” In my experience, they grow the same way your technical skills do. Through practice, reflection, and putting yourself in situations that stretch you a little. Two idea to help you get started:
- One of the easiest places to start is with small projects. When you build a portfolio project, run a home analysis, or take part in a hackathon, you’re not just learning tools; you’re practising problem-solving, communication, and time management.
- I’ve also seen people make huge progress by volunteering their growing data skills for a charity or local group. Real stakeholders, real deadlines, real conversations. It all helps.
A big part of what we coach learners on is how to evidence these soft skills. That might mean documenting how you handled a messy dataset, reflecting on how you adapted when a project changed direction, or showing the decisions you made along the way. If you want structured support, our range of data analytics courses gives you practical projects and guidance that bring these skills out naturally.
Bit by bit, these experiences add up, and employers notice.
Final Thoughts – Building These Skills Starts Today
You don’t need to have every soft skill perfected before you start a career in data. These abilities build over time, and most people develop them long before they feel “ready.” The important thing is to start small, pay attention to how you work, and give yourself chances to practise… whether that’s through a project, a course, or simply asking better questions.
If you want support while you’re building both your technical and soft skills, you’re welcome to speak with one of us at Learning People. We can walk you through training options, answer your questions, and help you figure out the next step that suits you.
Just hit the button below to book in a free call with one of our data career consultants.
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