Real World Data Scientist (Associate Director)

Astellas Pharma
Addlestone
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist – NLP & MLOps

Data Scientist - Inside IR35 - Hybrid

Data Scientist

Data Scientist

Senior Data Scientist

Data Scientist

Real World Data Scientist (Associate Director)

Join to apply for the Real World Data Scientist (Associate Director) role at Astellas Pharma


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, to ensure 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

Do you want to be part of an inclusive team that works to develop innovative therapies for patients? Every day, we are driven to develop and deliver innovative and effective new medicines to patients and physicians. If you want to be part of this exciting work, you belong at Astellas!


We are hiring an experienced real‑world data scientist to join our Real‑World Data Science (RWDS) team. As an Associate Director of RWDS, you will be an analytic researcher informing and conducting Real World Data (RWD) studies at any time in the drug lifecycle. You will work directly within the RWDS team to execute observational studies for internal and external consumption and partner closely with Development, Medical Affairs, and Pharmacovigilance/Pharmacoepidemiology colleagues in their research. Additionally, you will collaborate closely with others in RWDS, Biostatistics and the broader Quantitative Sciences & Evidence Generation department to enhance our RWD and analytics offerings. RWDS is multidisciplinary and provides RWE strategic input, study design, statistical and programming support to projects. Team members apply their unique knowledge, skills and experience in teams to deliver decision‑shaping real‑world evidence.


Hybrid Working

At Astellas we recognize the importance of work/life balance, and we are proud to offer a hybrid working solution allowing time to connect with colleagues at the office with the flexibility to also work from home. We believe this will optimise the most productive work environment for all employees to succeed and deliver. Hybrid work from certain locations may be permitted in accordance with Astellas’ Responsible Flexibility Guidelines.


Key Activities For This Role

  • Provide best‑in‑class data science support to Astellas drug development programs & marketed products in relation to RWD.
  • Design observational studies (primary and/or secondary data).
  • Execute (program and analyse) observational studies using in‑house RWD or oversee vendors or other RWDS staff in executing observational studies.
  • Write, review, or contribute to key study documents to ensure optimal methodological & statistical presentation. These documents include protocols, analysis plans, tables and figure (TLF) specifications, study reports, publications.
  • Ensure efficient planning, execution and reporting of analyses.
  • Advise as subject matter expert in specific data access partnerships.
  • Represent the company on matters related to RWD analysis at meetings with regulatory authorities, key opinion leaders and similar experts/bodies as needed.
  • Contribute to vendor selection with partner functions.
  • Participate in the creation and upkeep of best practices, tools/macros, and standards related to methods, data and data analysis at Astellas.
  • Collaborate with RWDS and Biostatistics colleagues and cross‑functional teams in Development, Medical Affairs and Pharmacovigilance.
  • Mentor and guide junior members of the RWD Analytics team.

Essential Knowledge & Experience

  • Proven pharmaceutical industry hands‑on experience in design and execution of observational studies and analysing RWD, including major EHR and claims databases from the US, UK, EU, and/or Japan.
  • Experience in analysing a diverse set of RWD study types (description, association, prediction, causation), including incidence & prevalence, treatment pattern, healthcare resource utilisation, cost, outcomes, and effectiveness.
  • Advanced and broad knowledge of statistical methods, along with understanding of industry practices & guidelines related to the analysis of RWD.
  • Proficiency in SQL, and SAS or R required, with working knowledge of Python beneficial.
  • Well‑versed in data visualization, statistical analysis, and machine learning methodologies.
  • Proficiency in non‑interventional research design for primary data collection (i.e., prospective) as well as secondary use of data.
  • Committed to seeking innovative methodology to generate data‑driven insights.
  • Experience working across geographies with globally distributed teams.
  • Self‑starting performer with the demonstrated capacity to operate both independently and collaboratively in a fast‑paced, team‑oriented setting.
  • Excellent communication and collaboration, and experienced working in cross‑functional teams.

Preferred Experience

  • Experience with the Oncology therapeutic area an advantage.

Education

  • A doctorate or master's degree in statistics, data science, pharmacoepidemiology or a similar discipline.

Additional Information

  • This is a permanent, full‑time position, based in the United Kingdom.
  • This position follows our hybrid working model. Role requires a blend of home and minimum once a quarter in office. Flexibility may be required in line with business needs.

Seniority Level

Mid‑Senior level


Employment Type

Full‑time


Job Function

Engineering and Information Technology


Industries

Pharmaceutical Manufacturing


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


Beware of recruitment scams impersonating Astellas recruiters or representatives. Authentic communication will only originate from an official Astellas LinkedIn profile or a verified company email address. If you encounter a fake profile or anything suspicious, report it promptly to LinkedIn’s support team through LinkedIn Help.


#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.

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.