Look, it’s a known fact that data science pays well for early-to-mid career professionals. That’s because it has such strong ties to measurable business outcomes. If you want good paying careers for women, then you’re already looking in the right area. However, that doesn’t quite answer the question: Is data science a good career for women beyond earning potential?
Let’s take a look at why this field can offer real fulfillment and is becoming a much more accessible culture for women. We’ll also delve into how it’s one of a select group of good careers for women that pay well and the potential it offers to move across different industries.
What makes data science a strong career choice for women right now?
Data analyst salaries speak for themselves. But what about how your life feels around the work? Autonomy, flexibility, growth and confidence are all key factors in the decision-making process. With that in mind, let’s look at four key reasons that makes data science a good career for women.
1. The job market rewards practical skill
Many people enter data science from business, economics, psychology, engineering, marketing, finance, and even humanities. What matters most is whether you can work with data, think clearly, and solve problems. That makes data science accessible if you’re switching careers or building a more technical profile without starting from zero.
2. You can build a portfolio that speaks for you
In data science, you can demonstrate ability through projects: dashboards, Python notebooks, case studies, experiments, and business analysis. That’s powerful because it makes career progress less dependent on who already knows you.
3. Your work can be visible and high-impact
Data science has a direct link to decision-making. If you want influence, not just tasks, this field can give it to you – especially as you grow into roles where you set metrics, shape product direction, or define strategy.
4. Leadership opportunities are expanding
Organizations increasingly need data leaders: people who understand analytics, AI, governance, and business realities. That opens long-term pathways into senior roles, team leadership, and strategic decision-making.
What skills do you need to succeed in data science?
There’s a lot to learn in data science. Luckily, you don’t need every skill upfront, but rather a foundation that you can build on over time. With that in mind, here are five hard skills that most entry-level roles ask for:
1. Data basics
You should feel comfortable working with messy datasets, making clean tables, and spotting issues like missing values or inconsistent definitions.
2. SQL for querying and analysis
SQL remains one of the most useful tools in analytics and data science because it helps you pull the right data fast and explore it efficiently.
3. Python for analysis and modeling
Python helps you clean data, run analysis, build models, and automate workflows. You don’t need to be a software engineer on day one, but you should be able to work confidently in notebooks and write clear code.
4. Statistics and experimentation
You’ll constantly evaluate performance, test hypotheses, measure outcomes, and interpret results. Solid statistical thinking helps you avoid overconfident conclusions.
5. Communication and stakeholder clarity
This is where careers accelerate. If you can explain what you did, why it matters, and what the next step should be, you become the person decision-makers trust.
What challenges should women expect in data science?
A current reality that we’re working against is that only 15-30% of data science professionals are women. This is really a perception problem – many women don’t even consider STEM careers because it’s seen as a cultural misfit. You’re probably thinking the same thing. And while women in data science do report common obstacles, none of them should stop you pushing ahead. In fact, they should provide more incentive to change things for the better.
With that in mind, let’s quickly look at those challenges before we offer you some solutions to keep you enthusiastic about a career in data science.
1. Lower confidence in the first role
When you join a technical team where you don’t see many people like you, it’s easy to second-guess yourself. The learning curve can feel steeper than it actually is, even when you’re doing well.
2. Imposter syndrome, even with strong performance
Data science moves fast and there’s always more to learn. That can make high achievers feel “behind,” even when their work is solid and their results are clear.
3. Limited mentorship in certain teams or industries
Some companies have strong coaching cultures. Others don’t. Without mentors or visible role models, it can be harder to navigate growth, ask the right questions, and map out a clear path forward.
4. Visibility gaps and uneven recognition
You can do excellent work and still not get the same sponsorship or credit as others. This often shows up in meetings, promotions, and who gets pulled into high-impact projects.
5. Biased hiring signals and higher pressure to prove yourself
Some candidates feel they need to check every box before applying, or deliver “perfect” proof to be taken seriously. That pressure can slow momentum, even when skills are already there.re.
Should you study data science at IE School of Science & Technology?
Now, solutions. If you’ve read through all of this and you feel data science might be for you, then we want to stay in touch. At IE School of Science & Technology, we’re actively seeking more female candidates for our master’s programs in STEM. Why? Because we know we can be a source of real change in society. We’re also proud to say we’re one of the few institutions worldwide achieving true gender balance in STEM, with women making up 51% of our most recent Master in Business Analytics & Data Science intake.
When you study with us, you develop skills across the data science workflow – from analysis and modeling to applied decision-making. You learn in a global environment that reflects how modern teams actually work, and you graduate with a profile you can take straight into the job market. Depending on your strengths and interests, you can use these skills to pursue roles like:
– Data Analyst
– Business Intelligence (BI) Analyst
– Product Analyst
– Machine Learning Analyst / Applied Data Scientist
– Data Scientist (generalist)
– Analytics Consultant
And by the conclusion of the program, you’ll feel confident in your skills and ready to change the industry. If you’re interested in learning more, scroll through more of our content (listed below) about the realities of working in the area, with breakdowns of salary, ethics and the benefits of higher education when job-hunting. Or, if you’re already sold on a career, click the button below and head straight to the program page for our Master in Business Analytics & Data Science. The journey starts today!
Want more guidance on steps forward? Read our guide on how to become a data scientist.
Interested in how much you can earn with a data analytics degree? Read our guide on data analyst salaries in Europe.
Thinking about going back to school? Read our guide on whether a business analytics degree is worth it.
Want to see how we support you in your studies? Read about our tech mentorship scheme.
Interested in our SciTech success stories? Read Leana’s journey to working in engineering at BMC Software.
Benjamin is the editor of Uncover IE. His writing is featured in the LAMDA Verse and Prose Anthology Vol. 19, The Primer and Moonflake Press. Benjamin provided translation for “FalseStuff: La Muerte de las Musas”, winner of Best Theatre Show at the Max Awards 2024.
Benjamin was shortlisted for the Bristol Old Vic Open Sessions 2016 and the Alpine Fellowship Writing Prize 2023.
