Clinical Data Enablement, Data Aggregation, Lead

Astellas Pharma Inc.
1 month ago
Create job alert

DescriptionClinical Data Enablement, Data Aggregation, LeadAbout Astellas: At Astellas, experience is coupled energised with a relentless challenger spirit. 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 are unusual in our ability to combine the experience, expertise and resources of an established company with the agility, flexibility and tenacity of a start-up. Relentless curiosity and a hunger for discovery flows throughout our entire organisation. We harness the latest technology and insights from big data with our research expertise to create powerful solutions that could transform the way doctors and nurses treat and care for their patients. We are accelerating product development, driving operational efficiencies and gaining a better understanding of the needs of patients and their healthcare providers. We partner and collaborate with academic research institutes and biotechnology companies who share our passion for bringing breakthrough discoveries to patients. The Opportunity: As the Clinical Data Enablement (CDE) Data Aggregation Lead, you will establish the data flow from source vendors to Astellas for Phase I-IV clinical trials executed by Astellas and transform that data for data review and cleaning purposes while also ensuring that these data are able to be shared to internal Astellas consumers following an established dataflow. Your role reports to the Head, Clinical Data Enablement, Data Science Clinical Data & Information Strategy. Your position is based in the UK or Republic of Ireland. Hybrid Working: At Astellas we recognise the importance of balancing your work and home life. This role offers a remote working solution so you can optimise the most productive work environment for you to succeed and deliver. Candidates interested in remote work are encouraged to apply. Key Responsibilities:

Enabling the data flow for Phase I-IV clinical trials consistent with aligned commitments inclusive of data ingest, transformation, and internal availability of this data for data review, data cleaning, and readiness for data analysis. Working directly with vendors providing source data (i.e. vendors providing lab results, ECGs, biomarkers, etc.) and/or internal functions such as clinical operations or early development to ensure that related specifications, mechanisms for data transfers, and data transfer schedules are established and executed appropriately. Contributing to related technical and/or process improvement initiatives associated with the clinical results data flow and efforts within the department and across Astellas broadly. Collaborating cross-functionally with areas performing data management, clinical operations, IT support, etc. to address any issues with the delivery or stability of the clinical data flow as appropriate.

Essential Knowledge & Experience: Prior pharma or CRO industry experience working on global clinical studies and projects or global process and system initiatives. Experienced with systems and processes that include an end-to-end data flow and the transformation of data in support of data review and/or data cleaning. Strong verbal and written communication skills. Demonstrated experience collaborating with others to achieve an objective. Familiarity with all phases of clinical development. Preferred Experience: Experienced in using at least one programming language (i.e. R, Python, SAS, SQL, etc). Demonstrated ability and experience in leading global process or system improvement projects. Knowledge of data standards in industry (CDISC, CDASH). Education/Qualifications: BS or MS degree, preferably in Computer Science, Informatics/Data Science, or life science discipline or equivalent. Additional Information: This is a permanent, full-time position. Limited travel requirement <5%. Primarily based in the UK or Republic of Ireland. This position follows our hybrid working model. Role requires a blend of home and a minimum of 1 day per quarter in our local 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, colour, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law.#LI-Dublin#LI-Hybrid#LI-JS1

Related Jobs

View all jobs

Clinical Data Manager

Lead Data Manager | Healthcare Sector | Cambridge

Clinical Trial Assistant (Undergraduate Placement Year)

Senior Clinical Information Scientist

Senior Bioinformatician

Senior Data Engineer

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Tips for Staying Inspired: How Machine Learning Pros Fuel Creativity and Innovation

Machine learning (ML) continues to reshape industries—from personalised e-commerce recommendations and autonomous vehicles to advanced healthcare diagnostics and predictive maintenance in manufacturing. Yet behind every revolutionary model lies a challenging and sometimes repetitive process: data cleaning, hyperparameter tuning, infrastructure management, stakeholder communications, and constant performance monitoring. It’s no wonder many ML professionals can experience creative fatigue or get stuck in the daily grind. So, how do machine learning experts keep their spark alive and continually generate fresh ideas? Below, you’ll find ten actionable strategies that successful ML engineers, data scientists, and research scientists use to stay innovative and push boundaries. Whether you’re an experienced practitioner or just breaking into the field, these tips can help you fuel creativity and discover new angles for solving complex problems.

Top 10 Machine Learning Career Myths Debunked: Key Facts for Aspiring Professionals

Machine learning (ML) has become one of the hottest fields in technology—touching everything from recommendation engines and self-driving cars to language translation and healthcare diagnostics. The immense potential of ML, combined with attractive compensation packages and high-profile success stories, has spurred countless professionals and students to explore this career path. Yet, despite the boom in demand and innovation, machine learning is not exempt from myths and misconceptions. At MachineLearningJobs.co.uk, we’ve had front-row seats to the real-life career journeys and hiring needs in this field. We see, time and again, that outdated assumptions—like needing a PhD from a top university or that ML is purely about deep neural networks—can mislead new entrants and even deter seasoned professionals from making a successful transition. If you’re curious about a career in machine learning or looking to take your existing ML expertise to the next level, this article is for you. Below, we debunk 10 of the most persistent myths about machine learning careers and offer a clear-eyed view of the essential skills, opportunities, and realistic paths forward. By the end, you’ll be better equipped to make informed decisions about your future in this dynamic and rewarding domain.

Global vs. Local: Comparing the UK Machine Learning Job Market to International Landscapes

How to evaluate opportunities, salaries, and work culture in machine learning across the UK, the US, Europe, and Asia Machine learning (ML) has rapidly transcended the research labs of academia to become a foundational pillar of modern technology. From recommendation engines and autonomous vehicles to fraud detection and personalised healthcare, machine learning techniques are increasingly ubiquitous, transforming how organisations operate. This surge in applications has fuelled an extraordinary global demand for ML professionals—data scientists, ML engineers, research scientists, and more. In this article, we’ll examine how the UK machine learning job market compares to prominent international hubs, including the United States, Europe, and Asia. We’ll explore hiring trends, salary ranges, workplace cultures, and the nuances of remote and overseas roles. Whether you’re a fresh graduate aiming to break into the field, a software engineer with an ML specialisation, or a seasoned professional seeking your next challenge, understanding the global ML landscape is essential for making an informed career move. By the end of this overview, you’ll be equipped with insights into which regions offer the best blend of salaries, work-life balance, and cutting-edge projects—plus practical tips on how to succeed in a domain that’s constantly evolving. Let’s dive in.