Head, Medical Affairs Statistical Science

Astellas Europe
10 months ago
Applications closed

Related Jobs

View all jobs

Head of Data Science -Telematics

Data Engineering Team Lead / Lead Data Engineer

Data Scientist Genomic Epidemiology - Pathogen

Data science programme lead, hireful

Data Engineering Manager (Data Platform)

Data Engineer

Head, Medical Affairs Statistical Science

About Astellas:

At Astellas we are a progressive health partner, delivering value and outcomes where needed. We pursue innovative science, focusing initially on the areas of greatest potential and then developing solutions where patient need is high, often in rare or under-served disease areas and in life-threatening or life-limiting diseases and conditions.

We work directly with patients, doctors, and health care professionals on the front line to ensure patient and clinical needs are guiding our development activities at every stage. Our global vision for Patient Centricity is to support the development of innovative health solutions through a deep understanding of the patient experience. At Astellas, Patient Centricity isn’t a buzzword - it’s a guiding principle for action. We believe all staff have a role to play in creating a patient-centric culture and integrating an awareness of the patient into our everyday working practices, regardless of our role, team, or division.

We work closely with regulatory authorities and payers to find new ways to ensure access to new therapies. We deliver the latest insights and real-world evidence to inform the best decisions for patients and their caregivers, ensuring the medicines we develop continue to provide meaningful outcomes. Beyond medicines, we support our stakeholder communities to drive initiatives that improve awareness, education, access, and ultimately standards of care.

The Opportunity:

As a Head, Statistical Science, you should have previous experience as a line manager and exceptional knowledge of the scientific area of responsibility. The position will require experience in complex and not well-understood statistical topics and/or disease states and/or RWD Science, as well as the ability to lead change and learn quickly technical topics.

You are also expected to consistently and significantly impact the technical capabilities of the department and the external scientific community. You will be responsible for supporting the key company priorities: BOLD, Growth Strategy and Sustainable Margin Transformation (SMT), which includes development and maintenance of excellence in portfolio, product and study design, optimal use of Real-World Data to support Medical Affairs tactics, and development, analysis and reporting standards, and innovative methodologies use.

You will work independently and involve the right level of participants as needed (cross-functional peers, Sr management, Primary Focus Statistics Lead (PFSL)) within or outside M&D. The position represents Astellas in interfaces with regulatory agencies globally, chairs sessions in international statistics meetings, participates in industry-wide technical discussions, and represents Astellas in professional societies.

Hybrid Working:

At Astellas, we recognize the importance of balancing your work and home life, so we offer a hybrid working solution allowing time to connect with colleagues in person at the office alongside the flexibility to work from home; optimizing the most productive work environment for you to succeed and deliver.

Key Activities for this role:

  • The position directs Astellas statistical strategy, creates and organizes new areas of methodological research, and is accountable to provide best-in-class data science support to Astellas drug development programs.
  • Advises project teams and statistical leads on challenging statistical design, analysis, and decision-making issues.
  • Performs/supervises modeling and simulations, leverages external experts to provide input to clinical development issues, and participates in due diligence activities, reviews data, and advises the company.
  • Presents and defends complex statistical solutions to internal governance committees, key stakeholders, or regulatory bodies in a compelling and impactful way.
  • Institutes best practices regarding planning, execution, interpretation, publication, communication, and regulatory/HTA submission of assets - Writes statistical position papers.
  • Challenges and influences peers and senior managers inside and outside of Data Science on best practices in their area of competence.
  • Coaches and supports statistical leads in providing best-in-class data science support to Astellas drug development programs.

Essential Knowledge & Experience:

  • Substantial years of experience in applying statistical methods in biomedical research, Pharma, CRO, Academia, or Healthcare industry or in providing statistical direction in these areas.
  • Understanding of pharmaceutical industry leading practices (e.g., regulatory framework, inspection process, HTA guidance, technologies, systems, etc.).
  • Advanced and broad knowledge of statistical methods, along with understanding of industry practices related to the statistical analysis of clinical data.
  • Knowledge and skills in SAS required, knowledge of R preferred.
  • Record of publications in clinical trials or methodological research in highly regarded peer review journals or invited speaking engagements.
  • Understand Real World Evidence, omics, digital endpoints, and Machine Learning.

Education/Qualifications:

  • PhD or M.S. in Biostatistics, Statistics, or related scientific field.

Additional information:

  • This is a permanent position in the United Kingdom or Canada.
  • Role requires a blend of home and minimum once a quarter in our Regional or Affiliate Astellas office. Flexibility may be required in line with business needs.
  • Candidates must be located within a commutable distance of the office.

We are an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law.

#J-18808-Ljbffr

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.

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

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

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

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.