Job description Posted 07 February 2019

Description

Data Quality Lead/ Clinical Data Scientist, Basic Qualifications:

Highly effective interpersonal skills with excellent written and verbal communications.

Demonstrated leadership skills within a matrix environment;

ability to influence and negotiate with stakeholders to identify win/win solutions.

Ability to apply scientific knowledge,

previous experience and curiosity, to identify risks to data integrity and implement mitigation plans to maximise data evaluability.

Highly organised with ability to prioritise work and remain focused on objectives in rapidly changing circumstances.

Preferred Qualifications: Experience would be preferred in any of the following:

Rare diseases,

Gene therapy,

Working with Academic Research Organisations (AROs),

CDISC and working with eDC tools (RAVE).

Conversant with the clinical trial environment, study design, and the bigger picture of drug development.

Ability to think outside the box within the confines of company procedures, and industry and regulatory requirements.

Demonstrated effective oversight of outsourced activities.

Details: Primary responsibility will be to assist with the oversight of CRO conducting the following

Activities: define, build & test database (eCRF) define, build & test data validation checks, data entry and clean-up (queries, coding, reconciliations) and delivery of clean datasets for analysis and reporting.

The role requires expert data management experience in the whole lifecycle of a clinical trial from database set-up to database lock, sound knowledge of ICH-GCP and good organisational skills. Lead study and project teams in the delivery of high-quality data to support the reporting of early phase clinical pharmacology studies.

Close collaboration with investigational sites and monitors around the world in the setup conduct and delivery of clinical trial data to the highest quality.

Provide data management input to the protocol and translating the protocol into specifications for all data types.

Utilisation of data visualisation tools to identify scientifically unfeasible or anomalous data and taking appropriate action to prevent or minimise reoccurrence.

In partnership with study team, responsible for delivery of data management contributions to regulatory submissions when required. As necessary, identify, participate and/or lead process improvement initiatives. Train, coach and mentor staff as required completion of specific project goals and objectives in accordance with defined quality and time-based metrics.

Additional information about the process

All profiles will be reviewed against the required skills and experience. Due to the high number of applications, we will only be able to respond to successful applicants in the first instance. We thank you for your interest and the time taken to apply.