About the Hiring Group:
RTI Health Solutions (RTI-HS), a wholly owned subsidiary of RTI International, is an independent and internationally recognized research organization. With offices in the US, UK, Spain, France, and Sweden, we provide healthcare consulting and research expertise to optimize decision making for pharmaceutical, biotechnology, and medical device products across the development and marketing lifecycle. Clients rely on our expertise, quality standards, and integrity to guide their product development and regulatory and market access strategies. Our various practice areas include Value, Access, and HEOR; Patient-Centered and Outcomes Research; Epidemiology and Biostatistics; Medical Communications; Global Business Operations; and Strategic Consulting and Growth.
We are seeking an Assistant Statistician to join our Evidence Synthesis and Statistics team. As an Assistant Statistician, you will work within an experienced and collaborative team, supporting the delivery of statistical analyses and evidence generation studies for pharmaceutical and healthcare clients.
Our team applies a range of statistical methods, including meta-analysis, network meta-analysis, survival analysis, and utility analysis, to evaluate the clinical, humanistic, and economic value of medicines and other healthcare interventions. This research is used by Health Technology Assessment authorities worldwide to inform decisions about the availability, pricing, and reimbursement of healthcare products.
This role offers the opportunity to develop technical expertise, contribute to scientific publications and conference presentations, and gain experience across a variety of research and consultancy projects.
This is a 12-month UK-based contract working in our Manchester office.
What You'll Do:
In this role, you will:
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Work under the guidance and direction of senior team members to:
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Develop statistical analysis plans, prepare analysis datasets, conduct analyses, and contribute to study reports. Analyses may include meta-analysis, network meta-analysis, survival analysis, and utility analysis.
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Support the delivery of high-quality scientific projects within agreed timelines and budgets.
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Contribute to the development of abstracts, posters, manuscripts, and other scientific publications through writing, reviewing, and content development.
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Support the development of client proposals and other business development activities.]
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Learn and apply new statistical methods while developing programming skills in R and other statistical software, including WinBUGS/OpenBUGS/JAGS and SAS/STATA.
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Work effectively as part of a cross-functional team, collaborating with statisticians, health economists, and systematic literature review specialists.
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Apply statistical methods to support research projects that inform healthcare decision-making.
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Communicate effectively with clients and colleagues via email, video conference, telephone, and in-person meetings.
What You'll Need:
- An undergraduate or advanced degree in mathematics, statistics, biostatistics, or a related field.
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Coursework in or experience performing statistical analyses that demonstrate strong quantitative aptitude.
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Programming experience with R or similar statistical software.
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Knowledge of fundamental statistical concepts, including statistical inference, regression, probability, and statistical distributions.
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Demonstrated ability to read, evaluate, and communicate research findings.
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Strong attention to detail and organisational skills, with the ability to manage multiple tasks, prioritise effectively, and adapt to changing project deadlines.
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Enthusiasm and commitment, with the ability to deliver consistently high-quality work within agreed timelines.
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Ability to work independently while also contributing effectively as part of a collaborative team environment.
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Willingness to learn from others, seek feedback, share knowledge, and contribute positively to team success.
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Excellent communication skills in English, both written and verbal, with the ability to explain technical information clearly to different audiences.
Desirable, but not essential
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Coursework or experience in one or more of the following areas: meta-analysis, network meta-analysis, survival analysis, retrospective data analysis, clinical trial analysis, and/or utility assessment.
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Experience using additional statistical software packages such as SAS, STATA, and/or WinBUGS.
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Knowledge of Bayesian statistics
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Familiarity with, or interest in, AI-assisted analytical tools and how technologies may be applied appropriately and effectively within statistical practice.
