Manager, Data Acquisition

Key Responsibilities Database programming and data acquisition: Design, write and maintain database programs and scripts in CQL, R and Python.

Use them to acquire, transform, reconcile and quality-check clinical and external vendor data (central labs, sequencing, biomarkers, imaging, eCOA/ePRO), and integrate it into GSK systems.

AI/ML and automation: Identify, evaluate and pilot AI/ML and automation, working with Data Management, Clinical teams.

Examples are ML-based anomaly detection beyond rule-based edit checks, AI-assisted Data mapping, NLP/OCR for unstructured data, and predictive risk insight.

Hold AI/ML programming awareness (Python/R ML libraries, model validation, bias and limitations) and be a credible partner to technical teams.

Delivery and vendor oversight: Deliver multiple clinical studies across all data acquisition activities, including timelines, oversight and governance of external vendors, and driving team members to meet milestones.

Study team advisory: Advise Study Core Team members on DM operational and technical requirements.

Provide input to study design, the protocol and study planning, and coordinate review and approval of specifications and vendor deliverables (including translations).

Issue resolution and reporting: Mediate and solve data acquisition issues, escalating as needed.

Report status and risks to the Study Data Manager, ensure quality control of deliverables, and run after-action reviews to share learnings.

Data standards and transformation: Apply CDISC/SDTM standards and develop programmatic transformations of clinical laboratory and vendor data.

Maintain data flow and database architecture integrity (Oracle, Unix).

Compliant and responsible AI/ML use: Ensure programmed and AI/ML-enabled solutions are validated, auditable and explainable, and comply with GxP, ICH-GCP, data privacy and GSK AI governance.

Keep human review in place for AI-generated outputs.

SME and capability building: Act as Subject Matter Expert for process improvement, new processes, training, quality assessments, audits and inspections.

Mentor junior team members in programming and emerging technologies, and line-manage a small team as required.

Outsourced studies and compliance: For e2e DM FSP or FSO studies, support the Study Data Manager and Oversight Data Manager on ePRO, eCOA and other vendor data matters.

Adhere to SOPs, corporate policies, regulatory requirements and industry best practice.

Education Bachelor's degree or equivalent in Engineering, Life Sciences, Computer Science, Mathematics, Physics or equivalent.

Needed to understand complex processes, study designs and systems in the pharmaceutical industry.

Experience 8+ years of experience in data acquisition or related roles in clinical research or information systems, and/or relevant scientific experience.

Demonstrated ability to deliver clinical data management tasks to time, quality and DM process compliance, with sufficient autonomy in highly complex study set-up with multiple stakeholders.

Ability to act as SME, mentor and technical advisor on complex concerns.

Preferred Hands-on experience applying analytics, automation or AI/ML in clinical data management or a related data-intensive environment.

Hard Skills Experienced in Data Management in the pharmaceutical industry; good understanding of regulations including ICH-GCP.

Experience with CDISC standards, including SDTM.

Database programming (Must): proficiency in SQL with working knowledge of R and Python for data extraction, transformation, reconciliation, validation and reporting.

Familiarity with other languages (e.g.

SAS, C#) is an advantage.

Experience working in database systems and environments; understanding of data flow and database architecture concepts.

AI/ML awareness (Must): working understanding of core AI/ML concepts (supervised/unsupervised learning, model training and validation, NLP, generative AI/LLMs), their limitations (bias, hallucination, data quality) and appropriate use cases in clinical data acquisition.

Familiarity with Python/R ML libraries is an advantage.

Expertise in eDC solutions (InForm, Rave, Veeva CDMS/Vault) and ePRO/eCOA solutions.

Experience developing programmatic transformations of clinical laboratory data; knowledge of medical terminology related to biospecimen collection and assessment.

Knowledge of data types such as central labs, RNA/DNA sequencing, flow cytometry, complex biomarkers, eCOA, microscopy, immunohistochemistry, ocular assessments and imaging.

Ability to explain complex technical principles to non-technical staff; experience managing business remotely in a matrix network.

Soft Skills Excellent communication skills in a matrix environment; presentation and training skills; fluent English (written and spoken).

Good analytical, problem-solving and project delivery skills; results oriented and customer focused.

Strong ability to mentor teams; team spirit and ability to facilitate interfaces across an extended network.

Good time management; ability to anticipate change and implement it flexibly; proactive and stress resistant.

Able to manage complex planning conflicts in multi-disciplinary meetings and propose pragmatic solutions.

Innovative, process-minded and open to new technologies; quick learner with curiosity about emerging tools such as AI/ML.

Skills Why GSK?

Uniting science, technology and talent to get ahead of disease together.

GSK is a global biopharma company with a purpose to unite science, technology and talent to get ahead of disease together.

We aim to positively impact the health of 2.5 billion people by the end of the decade, as a successful, growing company where people can thrive.

We get ahead of disease by preventing and treating it with innovation in specialty medicines and vaccines.

We focus on four therapeutic areas: respiratory, immunology and inflammation; oncology; HIV; and infectious diseases – to impact health at scale.

People and patients around the world count on the medicines and