Mathematician

Scientis Search Ltd
Bicester
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist, Sports

Senior Data Scientist, Sports

Lead Data Scientist

Computer Vision and Artificial Intelligence Engineer

Faculty Fellowship Programme - Data Science - May 2026

Lead Data Scientist - Customer Development

An award-winning Market Access Consultancy, renowned for creativity and innovation, is looking for a mathematician to join their cutting-edge Operational Research Team as an Operational Researcher. This is an exceptional opportunity for an experienced mathematician, skilled at developing bespoke numerical / mathematical solutions, to join an incredible Operational Research team dedicated to developing beautiful solutions to complex real-world problems.


The company

The company, a leading Market Access Consultancy, are passionate about science and innovation in health technology, moreover they are interested in the real-world impact of such innovations. The company employs the best creative talent from the HEOR Space, brilliant consultants who are genuinely excited by what they do and the positive impact their work has. They thrive in engaging with clients, overcoming market access challenges, and contributing to patient access to innovative medicines, diagnostics, medical devices and healthcare delivery across a variety of disease areas.


The company knows that their success is down to the talented people they have working for them. To support their employees to reach their full potential they provided an excellent working environment, opportunities for career development, put a huge emphasis on a healthy work/life balance and of course offer an attractive renumeration package.


The Operational Research Team

Operational research (OR) is a systematic and scientific method for solving problems. The challenges faced in OR are often intricate and shrouded in uncertainty. As an Operational Researcher, you'll leverage cutting-edge analytics, modeling, problem structuring, simulation, optimization, and data science to uncover the best possible solutions.


The OR group is the organisations newest group and works on internal company and external client solutions. Collaborating with the company’s delivery teams, the OR group will explore how OR methodologies can enhance the quality, robustness, and timeliness of client deliverables. Additionally, they aim to develop innovative solutions to client challenges, creating resources that can be easily leveraged across projects. This approach ensures that the company’s technical teams can efficiently deliver high-quality results for all client initiatives.


Equipped with a deep understanding of cutting-edge design and analysis methods—both qualitative and quantitative—all members of the OR group will skillfully communicate complex concepts to their colleagues within the organisation.


Key responsibilities:

  • Translate project objectives into mathematical and/or analytic models, and effectively communicate proposed solutions internally and externally, and support in the proposal, implementation, delivery, documentation, and communication of complex solutions.
  • Collaborate with other teams to identify potential efficiency savings, and subsequently design and implement solutions to optimise workflow efficiency across the company.
  • Work with senior staff and subject matter experts to generalise client deliverables into stand-alone assets that can be re-used and further developed.
  • Collaborate with senior staff to develop analyses and provide technical leadership to support on-going thought leadership and marketing.
  • Liaise and collaborate with the Technology Working Group to provide technical expertise and ensure that technological advancements are aligned with company workflows and client needs.
  • Champion the creation of IP and ensure it is embedded across the organisation.
  • Develop best practice documentation and support senior staff to encourage uptake across the business.


Requirements

Essential

  • A MSc OR PhD in Mathematics, Operational Research, Physics OR Engineering.
  • Strong knowledge of R, Python, MATLAB, or C++
  • Strong mathematical literacy - comfortable with understanding complex systems of equations
  • Experience working with large, messy data
  • Experience of using mathematical optimisation.
  • Experience with numerical modelling and discretisation methods.


Desirable

  • Knowledge of R, or experience learning multiple programming languages.
  • Experience writing academic manuscripts.
  • Experience with Bayesian approaches.
  • Strong statistical knowledge.
  • Experience crafting compelling narratives from complex results for non-technical audiences.
  • Knowledge of time to event analysis.
  • Experience working in the health or pharmaceutical industry.
  • Experience working in a consulting environment.


Benefits

Competitive compensation and benefits package, including:

  • A ‘learning’ culture focused on personal development and supported by study bursaries
  • Workplace pension scheme
  • Private health insurance with AXA Health
  • Range of high street, supermarket, restaurant, gym membership, holiday and entertainment discounts via Sodexho
  • Cycle to work scheme
  • Employee assistance programme
  • Employees are given an additional day of leave for: their wedding and moving house
  • Annual leave purchase scheme of up to 10 additional days’ leave per year

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.