At Faculty, we believe diversity of individuals working together fosters diversity of thought, and this is the bedrock of true innovation. The application of AI to every facet of our lives is a once-in-an- epoch opportunity – it is critical that women are equal authors and leaders in its development and deployment. One of our core company principles is to ‘seek truth’. We recognise the potential for AI to evolve in ways which amplify and serve only the most dominant voices of our time; inherent in our commitment to countering this is to ensure we have a diverse workforce spearheading the transformative work we do in applied AI.
At Faculty, our decade of AI experience and work with over 350 customers have provided deep insights into the evolving data landscape. Over the next five years, AI will profoundly impact the UK’s data environment. We expect three key lessons to gain traction.
First: AI should enhance, not replace, human decision-making. The past focus on “data-led” decisions has often led to data overload with few actionable insights. AI can offer targeted decision support, but it must be explainable to ensure human accountability.
Second: the idea of “data” as a standalone asset is flawed. Effective AI requires relevant, problem-specific data. Many organisations err by prioritising data collection and infrastructure before clearly defining their problem and technology needs. This misconception will diminish as organisations realise its limitations.
Lastly, the emphasis should be on “data science” not just “data.” While data fuels AI, data alone solves no problem. Data alone can’t help you build an understanding of the world. It’s the science that you do on top of the data that matters the most. As organisations better understand this, they will more effectively harness AI.
At Faculty, we believe in the power and necessity of human-led AI to transform the world for good.
Founded in 2014, we saw the potential of AI long before the current hype-cycle. Our story started with our Fellowship programme, which trains the brightest STEM PhD and master’s graduates to transition from academia to a career in data science.
Since then, we’ve evolved to become one of the world’s leading providers of human-first AI solutions. We have spent the last ten years at the frontier of applied AI – optimising the performance of the organisations that shape our world.
With an MSc in Industrial Engineering, I have always been passionate about finding data-driven solutions to real-world problems. Early in my career, I helped develop a predictive maintenance solution for wind turbines at ZF Wind Power in Belgium, which deepened my understanding of how powerful data can be when applied effectively.
My interest in using data to tackle global challenges also led me to research machine learning for net-zero emissions, which I’ve had the opportunity to present at international conferences, with a focus on decarbonising emerging economies. Today, I’m a Data Scientist at Faculty AI, where I work on AI safety and security for government clients.
One piece of advice I’d give to other women in data is to stay curious and open to learning across different areas. Adaptability is key in this space, and through continuous learning, we can unlock the full potential of data and make a real impact. I’m excited to continue this journey with all of you, breaking barriers and supporting each other along the way!
After graduating as a Civil Engineer & getting rejected by over 20 civil engineering consultancies and construction companies, I started my professional career as an Analytics Analyst at Accenture, the only job offer I had. I learned the fundamentals of technology, data, AI and software engineering from the ground up during my 2-year tenure. Eventually, I landed a role at Faculty as a Machine Learning Engineer.
At Faculty, I build Data and AI products for clients in the public and healthcare sectors, with a current focus on the Energy Transition space.
One of my favourite aspects about my current role is the variety it offers. I relish the opportunity to apply creativity through Data and AI across different industries, from business development to project ideation and to implementation and delivering solutions to users. With the rapid evolution of Data and AI technologies, I frequently get to engage with new advancements, which adds to the variety and keeps my work exciting.
I consider myself very fortunate to have caught the Data and AI wave in the right place at the right time, and I have had my fair share of learnings over the years. I also recognise that I would not have made it this far without the mentorship of others. As such, I love to share my candid experiences and mentor others in my free time.
Dr Mimi Das is a data scientist who is passionate about evidence-based decision-making. Trained as a physicist from the University of Cambridge, she applies mathematics to a range of problems in the public health and life sciences sector, ranging from modelling disease transmission to predicting how long a patient may stay in a hospital.
Currently, Mimi is working on delivering Faculty’s decision intelligence platform, Frontier, to customers. Prior to joining Faculty, she was a senior epidemiologist in the UK Health Security Agency, conducting statistical analyses on risk factors for influenza and COVID. She has a PhD in mathematical epidemiology from the Swiss Tropical and Public Health Institute, specialising in modelling interventions against malaria and neglected tropical diseases, particularly in low-resource settings.
My journey in STEM began with physics, culminating in a PhD before I pivoted into the world of data science. What I love most about working in data is the fact that I am constantly learning. It’s a field that never stands still – technology evolves rapidly, and there’s always new tools and techniques to master. This creates an environment where your brain is constantly whirring in the best possible way!
I’m grateful to live in an era where significant strides are being made for women’s rights. The doors to education and career paths that were often closed to previous generations now stand open. Yet, despite this progress, I’ve often found myself to be the only woman in the room throughout my career. Although navigating these kinds of spaces hasn’t been easy, it has ultimately made me stronger. To other women in data or considering this path: your voice and viewpoint are invaluable, so believe in yourself!
My love for data began during my Biomedical Engineering degree in Barcelona, where I discovered the power of programming. I was fascinated by how data could reveal hidden patterns—like predicting how much force a leg can withstand before breaking or identifying tumours from medical images. That’s when I knew I wanted to become a data scientist.
Since then, I’ve had the opportunity to work as a data scientist in several industries, from a biotechnology institute in Alabama to a digital health startup and a market research firm in London. Currently, I’m a Senior Data Scientist at Faculty, where I develop AI models for government clients. It’s incredibly rewarding to witness the transformative power of data across organisations.
For many years, data careers were predominantly male, but that’s changing! At Faculty, I’m fortunate to work with a talented team of female data scientists and engineers. We constantly learn from each other, and it’s exciting to see more women in the field.
My advice to women pursuing a career in data is to persevere and find out what you love. There are plenty of different opportunities in the field for those who are passionate and curious. Don’t be afraid to take risks and carve your own path.
I’ve been lucky to have a varied career in a range of different data roles, making my way to data science via research, statistics, analysis and data product management. I have over ten years’ experience in machine learning and data science in industry, startups, government and academia, including a PhD from Oxford University, leading the data team at a startup, and building and deploying language models at Amazon Alexa.
I currently work as a Senior Data Scientist at Faculty, an applied AI consultancy, where I lead technical teams on projects in sectors including finance, retail, insurance, legal tech, healthcare and government.
My best advice to anyone building a career in data is that careers are long and it’s ok for your career path to be winding. Explore opportunities that interest you, even when they’re outside of your comfort zone or even your job role. I’ve often learnt the most from doing things that ‘aren’t my job’. As a woman in data science, having a wide variety of skills and experience to draw on has helped give me confidence in situations where I’m the only woman in a room and I’m hit by imposter syndrome. So grab opportunities to try new things!
Six years ago, after flicking through a teach-yourself-Python book in Waterstones, I decided to change careers there and then. I went from working in Communications to getting a job on an IT Helpdesk and then transitioning to Data Analysis. During my year in IT, I started a Data Science degree with the Open University which I am still studying in my spare time and have been a fully-fledged Data Analyst for almost 4 years. In that time, I have learnt how to use Power BI, SQL, Tableau and Python within my day job and have studied computer science, statistics, data structures and algorithms within my course. I also volunteer with DataKind – a non-profit which hosts 2-day hackathons for data experts to work with charities to help them advance their data projects.
I love learning, and working in data gives you the opportunity to constantly develop new skills and try out new software. I also don’t regret my previous degree and career choice as everything I learnt and experienced while working in communications has only helped to enhance my ability to tell stories with data today! When I first started out, I was inspired by the profiles of the women featured in Women in Data and so I am very grateful for the opportunity to be featured myself as a representative for Faculty and for Women in Data.
