From mathematics to data science: building trusted professional standards

Summary

• Mathematicians will have strengths across data science, but key transferable skills include logical thinking, problem solving, and rigorous solution development
• Data science increasingly influences lives, and multiple professional organisations including the AfDSP and BCS are working towards recognised standards
• Formal accreditation demonstrates commitment, capability and professionalism, and multiple accreditations can reflect an expansion of expertise gained through a growing career
• Formal professional accreditation builds trust with the public, which is even more important in the digital age as data science's influence expands

Edward Rochead has spent more than two decades applying mathematics to some of the government’s toughest challenges. Now Deputy Head of the MOD Science Function and Chair of the Alliance for Data Science Professionals, he explores how the combination of mathematics, data analytics and professional oversight can support a safer, fairer and more dependable digital landscape. 

Please introduce yourself and provide some background on your career.

I've worked in government since graduating from Loughborough University with a Master of Mathematics degree in the late 1990s. I began my career as a mathematical modeller in the Ministry of Defence, developing models to support analysis and decision making, before moving into operational research, applying mathematics to improve business decisions across defence.

Over time, I took on roles in strategic partnerships, programme management and research commissioning, eventually becoming chief mathematician of the Defence Science and Technology Laboratory. Today, as Deputy Head of the MOD Science Function, I focus on the foundations that enable defence science to succeed, including skills, data, professionalism and assurance.

Alongside my government career, I've remained active in professional bodies. I'm a Chartered Mathematician and Fellow of the Institute of Mathematics and its Applications, and I currently chair the Alliance for Data Science Professionals (AfDSP), which brings together organisations including BCS to maintain professional standards for data science practitioners.

What motivated your shift from a more traditional mathematics or operational research career into engaging with data science, and how did that transition shape your professional identity?

I can trace the turning point to a very specific moment. In 2017, I attended an event at the Royal Society exploring the power of data. Speakers from organisations as diverse as the R&A (which defines golf standards), Dyson, and DreamWorks explained how data was transforming the effectiveness of their work. One example in particular stayed with me. Lincoln Wallen, then at DreamWorks, explained how data-driven digital engineering had enabled the company to reduce production costs by 35% while increasing productivity by three orders of magnitude. Every presentation demonstrated the power of data to solve problems and create value.

What struck me was that, although the event was framed around mathematics and innovation, almost every example was fundamentally data driven. That was the moment I realised just how important data had become, not only to mathematics, but to virtually every STEM discipline.

That experience prompted me to become more digitally and data literate, to engage more closely with the data science community, and eventually to become involved with BCS and the Alliance for Data Science Professionals.

Today, I sometimes describe myself as a ‘mathematical engineer’ — someone who uses mathematics to help improve systems, organisations and society. It reflects my own evolution from being primarily an applied mathematician to working much more broadly within the data and digital community.

From your perspective, what skills or ways of thinking transfer most effectively into data science?

Many mathematicians already possess a significant proportion of the skills needed for data science, but there isn’t a single pathway. I tend to think of data science as bringing together three key elements. First, there is access to, management of and ethical stewardship of data. Second, there are the mathematical and statistical algorithms applied to that data. Third, there is the computing and coding needed to make everything work in practice.

Different mathematics graduates arrive with different strengths across those areas. Someone with a strong statistical background may already be very comfortable with data analysis and modelling, but may need to strengthen their computing skills. Others may have substantial programming experience but require additional training in statistics and data science methods.

The common thread is that mathematicians are trained to think logically, tackle complex problems and develop rigorous solutions. Those skills transfer extremely well. The challenge is identifying your personal gaps and finding ways to address them through professional development and accreditation.

Data science draws from multiple disciplines. How important is it to establish a recognised professional standard across such a diverse field?

I think it’s absolutely vital. Data science increasingly influences decisions that directly affect people’s lives. If it is carried out poorly or without appropriate ethical considerations, real harm can result. Professional standards help assure that practitioners have the knowledge, competence and ethical awareness needed to work responsibly. A recognised professional standard creates a common benchmark.

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There are practical benefits too. It helps employers identify capable professionals, supports recruitment and career progression, and gives practitioners a clear framework for development.

One of the strengths of the AfDSP is that it brings together multiple professional bodies, including BCS, the Royal Statistical Society and others, to work towards common standards. That collaborative approach preserves the diversity that makes data science such an innovative field while ensuring consistency in professional expectations.

Cross-disciplinary working is one of the greatest strengths of data science, and common standards help us retain that advantage.

For mathematicians or other STEM professionals considering a move into data science, how valuable is formal accreditation, such as the AfDSP, in demonstrating credibility and competence?

Formal accreditation can be incredibly valuable. One of the early AfDSP registrants described it as giving them a ‘professional home’. In their case, they had originally trained as a statistician but no longer felt that traditional statistical accreditation fully reflected the breadth of their work. The data science accreditation recognised the wider combination of skills and responsibilities they had developed throughout their career.

I think that experience resonates with many professionals. People often evolve beyond a single discipline as their careers progress. Data science accreditation provides a way of recognising broader expertise while still maintaining links to established professional bodies, such as BCS.

For individuals, accreditation demonstrates professional commitment, capability and competence. For employers, it offers external validation that someone’s knowledge and skills have been independently assessed against recognised standards. That can be particularly valuable when organisations are seeking trusted practitioners to lead increasingly important data-driven activities.

In a rapidly evolving field like data science and AI, how important is CPD in maintaining both technical expertise and ethical standards?

Data science and AI are changing at an extraordinary pace. New tools, methods and applications emerge constantly, which means professionals have to keep learning throughout their careers if they want to remain effective. Equally important is the ethical dimension. When I studied mathematics, ethics barely featured in the curriculum; today, that would be unthinkable. Ethical awareness is now a fundamental professional competency.

I believe that professional bodies have a crucial role to play here. They provide access to ethics training, specialist events, conferences, seminars, mentoring and professional networks. Those opportunities often expose practitioners to ideas and experiences they would never encounter within their own organisation. For example, I recently discussed AI applications in both the power sector and flood management. Although those sectors are very different from my own, the conversations generated insights that could be applied elsewhere.

Professional bodies also provide access to mentors and peer networks. This can be especially important for people working in smaller organisations, where a senior specialist may not be available to support their development. CPD through a professional body, therefore, complements employer-led training by giving members access to a much broader professional community.

Ultimately, how do you see professional accreditation helping to build trust in data-driven work?

At its core, professional accreditation helps demonstrate that practitioners are committed to working competently and ethically. It reinforces the principle that technical capability alone is not enough; professionals must also understand their responsibilities and the wider impact of their work. As AI and data-driven technologies become increasingly embedded in everyday life, public confidence will depend not only on the systems themselves but also on trust in the people who develop and use them. Professional accreditation helps provide that assurance. It shows that individuals have met recognised standards and that ethical practice forms part of their professional identity.

In many ways, that reflects one of the traditional foundations of professional status. For generations, professions have earned public trust by demonstrating competence, accountability and ethical behaviour. Through initiatives such as the AfDSP, the data science community is applying those same principles to the challenges and opportunities of the digital age.