Associate Director, Biostatistics

Cytel
1 year ago
Applications closed

Related Jobs

View all jobs

Research Data Analytics Expert

Research Data Analytics Expert

Research Data Analytics Expert

Research Data Analytics Expert

Research Data Analytics Expert

Research Data Analytics Expert

At Cytel, we work hard to create successful careers with significant professional growth for our employees, as a result of which they work hard to make Cytel successful. Cytel is a place where talent, experience and integrity come together to advance the state of clinical development. 

TheAssociate Director, of Biostatistics, is responsible for supervising a team of biostatisticians and ensuring that their staff's project deliverables are successful, timely, and of high quality; may be responsible for staffing client projects with appropriately trained professionals retaining and developing these professionals within the organization. The Associate Director of biostatistics, may be responsible for promoting new business through participation in project proposals and client presentations. The Associate Director of biostatistics, is responsible for providing expertise and guidance to project teams, also related to project management and project financials, and may also act as the primary point of contact for clients in strategic partnerships.

Responsibilities:

Provide statistical expertise for DMC work. Direct activities across multiple project teams.

Skills:

Advanced knowledge of statistical methodology and analytic techniques. Conversant in SAS and/or R programming and associated processes. Conversant in FDA and ICH regulations and industry applicable standards. Conversant with aspects of the pharmaceutical industry including understanding of clinical drug development process and associated documents. Excellent oral and written communication skills. Ability to handle large volumes of complex tasks. Strong personal effectiveness and interaction skills. Highly motivated by team environment. Ability to lead multi-project teams.

Qualifications:

PhD / MS in Biostatistics, Statistics, or related discipline. A minimum of 8 years of relevant technical and leadership experience in a biometrics role, with experience in a managerial role. Prior DMC independent statistician experience required.

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 Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

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

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

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

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.