Why do women working in research make great data scientists in industry?

The demand for data scientists continues to grow, with industries increasingly relying on data-driven insights to make critical decisions. Interestingly, individuals with research backgrounds—particularly women, trans, and non-binary individuals—bring a unique set of skills that makes them particularly well-suited for data science and research roles.

Research roles typically require deep analytical thinking, the ability to manage and interpret complex datasets, and a rigorous problem—solving approach—all essential data science skills.

At CodeOp, a school focused on training women, trans, and non-binary individuals (women+) in tech, we’ve observed that those with research backgrounds often excel in our data science programs. Let me tell you why.

1. Analytical and Critical Thinking Skills

Researchers, especially those in scientific fields, are trained to approach problems systematically. This involves formulating hypotheses, designing experiments or studies to test these hypotheses, and analysing the results to draw meaningful conclusions.

Such a rigorous approach is directly transferable to data science, where problem-solving requires breaking down complex issues.

According to a 2022 report by the World Economic Forum, analytical thinking and innovation are among the top skills employers seek, with 73% of surveyed companies indicating their critical importance in the coming years.

2. Experience with Complex Data

Researchers often work with large, complex datasets requiring careful management, cleaning, and analysis.

Whether it’s genomic data or social science survey results, researchers are accustomed to handling datasets that are not only large but also messy and multifaceted.

Moreover, research-based roles often involve designing data collection methods, ensuring data integrity, and applying statistical analysis techniques—skills directly applicable to the data science industry.

3. Proficiency in Programming and Technical Tools

Researchers frequently use languages such as Python, R, or MATLAB for data analysis, simulation, and modelling. Additionally, many are adept at using statistical software (e.g., SPSS, SAS) and data visualisation tools to present their findings clearly and compellingly.

This technical proficiency is a critical asset in data science. According to a 2024 report by Burtch Works, 87% of data science positions require knowledge of at least one programming language, with Python being the most commonly cited.

Women+ with strong technical skills from their research backgrounds are, therefore, well-equipped to thrive in the dynamic and evolving field of data science.

4. Project Management and Collaboration Skills

Researchers typically plan and execute complex projects adhering to strict timelines.

These project management skills are directly transferable to data science roles, where managing data-driven projects from inception to deployment is key to delivering actionable insights.

Moreover, research is rarely a solitary endeavour. Collaboration with other researchers, departments, and sometimes external partners is essential.

Data scientists often work closely with software engineers, product managers, and business analysts to ensure their models and analyses align with broader organisational goals.

A report by McKinsey in 2023 highlighted that 62% of data science projects fail due to poor communication and misalignment between teams.

5. Adaptability and Continuous Learning

Research environments are dynamic, often requiring individuals to adapt to new methodologies, technologies, and scientific discoveries. This adaptability is a crucial trait in the fast-evolving field of data science.

Researchers, accustomed to keeping pace with developments in their field, naturally bring this mindset to data science.

Women+ researchers entering data science are well-prepared for this reality, having already cultivated a habit of continuous learning and adapting to the situation in their previous roles.

6. Success Stories and Industry Examples

The transition from research to data science is not just theoretical—many women, trans, and non-binary individuals have successfully made this shift.

Maria Carolina Rinaldi: From Clinical Psychology to Tech

Maria’s journey showcases the powerful transfer of skills from a traditionally non-technical field into the world of technology.

With a background in clinical psychology, Maria developed a deep understanding of human behaviour, empathy, and complex problem-solving. However, she was always fascinated by the tech world and wanted to be a part of it.

Through her studies at CodeOp, she embraced technology’s creativity and problem-solving aspects, leveraging her psychological expertise to approach coding from a unique, people-centric perspective.

Kellie Dixon: From Education to Quality Assurance in Tech

As a former teacher, she focused on gamifying classroom experiences and using tech tools to enhance learning, but her frustrations with user-unfriendly tools inspired her to shift into tech.

Driven by her desire to create more accessible tools for teachers and students, Kellie embarked on her coding journey.

After joining CodeOp, Kellie found a supportive community that empowered her to pursue her dream. Despite facing initial setbacks and challenges, Kellie’s unwavering persistence paid off as she secured a role as a QA Intern at Edurino, an EdTech startup.

7. Encouragement and Resources for Transition

For women+ individuals considering a transition from research to data science, the journey might seem daunting. Still, numerous resources and support networks are available to facilitate this change.

First and foremost, structured learning programs like CodeOp’s Data Science Bootcamp offers a comprehensive pathway to gaining the technical skills and industry knowledge needed for a successful transition.

Tailored specifically for women, trans, and non-binary individuals who are wanting to transition into the tech industry, CodeOp provides a supportive environment with mentors, career advisors, and a network of like-minded peers.

Additionally, online platforms such as Coursera, edX, and Udemy offer Python, machine learning, and data analytics courses, allow individuals to upskill at their own pace.