Senior Research Fellow (Statistical Analyst)

£52,413 (pending pay award) + benefits

Full-time, fixed-term contract until 31 August 2028

Help shape the future of teacher development by creating powerful opportunities to understand what supports great teaching, how teachers develop over time, and how evidence can inform better decisions across schools, policy and the wider education sector.

At the National Institute of Teaching, we believe teaching is a fundamental societal good, a privilege and a responsibility. Our work is rooted in that belief: we exist to improve the quality of teacher and leader development at a system level, so that every child and young person benefits from excellent teaching.

We are looking for a Senior Research Fellow (Statistical Analyst) to play a leading role in one of the most exciting and ambitious areas of our work: the development and use of the NIoT’s Teacher Education Dataset (TED). This growing national asset safely links pupil- and teacher-level data at scale, creating powerful opportunities to understand what supports great teaching, how teachers develop over time, and how evidence can inform better decisions across schools, policy and the wider education sector.

This is a rare opportunity to use advanced statistical analysis to answer questions that matter. You will lead complex quantitative research, shape how TED is used across the organisation and beyond and help turn large-scale data into insights that are rigorous, practical and meaningful for schools, policymakers and researchers. Your work will contribute directly to our mission by helping the sector better understand teaching’s impact and how teacher and leader development can be strengthened.

You will join a collaborative academic community of researchers, practitioners, digital product specialists and school-facing colleagues who are united by a shared commitment to evidence-informed improvement. You will work across the NIoT and with external partners including the Department for Education, UCL, the University of Oxford and Evidence Based Education, helping to ensure the questions we ask, and the evidence we generate, are relevant, robust and useful.

We are looking for someone with strong statistical reasoning, experience of quantitative research, and the ability to communicate complex findings clearly to different audiences. You will be confident working with large-scale data, developing reproducible research approaches, and supporting others to engage with quantitative evidence in thoughtful and accessible ways.

If you are motivated by the opportunity to apply your statistical expertise to work with real-world impact, and if you share our belief that teaching is both a societal good and a profound responsibility, we would love to hear from you.

This is a hybrid role, with the requirement for in person working for a minimum of once a fortnight, occasionally this will be more due to business need. The role includes participation in our annual all-staff away day (including an overnight stay) and occasional national travel.

We think a wide range of different work and educational experiences could support you to be successful in this role. We encourage applications from all backgrounds, communities and industries, and are committed to employing a team that has diverse skills, experiences and abilities. 

Essential criteria:

• Doctoral degree or equivalent body of work in education (or similar), applying statistical methods, quantitative analysis, econometrics, or similar.
• Strong statistical reasoning skills and familiarity with advanced methods for analysis of cross-sectional and cohort (panel) data.
• Familiarity with R for writing code.
• Experience with reproducible research pipelines and transparent code sharing standards.
• Ability to learn and adapt quickly, take the initiative in the face of new challenges, work both collaboratively and independently.
• Strong skills in communicating complex research results to different audiences, adhering to high professional standards.
• Motivated to work at the interface of academic and professional priorities and working cultures with a diverse team with multiple external stakeholders.
• Commitment to the NIoT’s mission and to improving outcomes through rigorous evidence, strong partnerships and thoughtful external engagement.
• Familiarity with using Git and GitHub for reproducible code and research.
• Experience of supporting governance reviews, governance effectiveness activity or governance improvement programmes.
• Experience of research governance and/or research ethics processes.
• Familiarity with the regulatory framework applicable to higher education providers, including Office for Students requirements and sector governance guidance.

Desirable criteria:

• Familiarity with schools’ data, or education data generally.
• Familiarity with value-added modelling, multi-level modelling, and using routinely collected quantitative data to make practical and policy-relevant inferences.
• Experience working as a teacher or in another role in schools.

Key benefits:

• Generous annual leave – 27 days’ holiday a year (plus 8 bank holidays), rising to 33 days after five years’ service.
• Flexible working – we offer flexible start and end working times, with hybrid working in place for all roles.
• Pension - Entry to the Local Government Pension Scheme.
• Salary sacrifice schemes – we offer salary sacrifice schemes for bikes, cars and tech!
• Lifestyle benefits – We offer discounts on gyms, cinema, retail and much more!
• Parenthood leave – We offer above the statutory minimum for maternity, adoption and paternity leave.
• Working environment – We have a stimulating, supportive and rewarding working environment with a dedicated team of like-minded professionals.
• Support: Our Employee Assistance Programme (EAP) provides confidential support for employees on personal or work-related matters.
• Development: We fully support the development of our staff and ensure that you have a high level of continuous professional development.

If you have any queries about this role, please email us at recruitment@niot.org.uk

Closing date: 14 September 2026.

Interview date: w/c 21 September 2026.

We reserve the right to close this vacancy early if we receive a high volume of applications.

Please note that applications should be written by the candidate. Where we have reason to believe that an application has been generated or substantially written by artificial intelligence, we reserve the right to reject it.

Candidates must already have the right to work in the UK, as we are unable to offer visa sponsorship for this role.

This post requires a satisfactory enhanced disclosure from the Disclosure and Barring Service (DBS) with a Children’s Barred List Check and Occupational Health Check is required as a condition of employment.

£52,413 pending pay award