Offer summary

Qualifications:

BS or MS in Life Sciences, Data/Computer Science, Excellent communication and collaboration skills, Knowledge of CDISC data standards, Project Management skills, Leadership capabilities and decision-making.

Key responsabilities:

Collaborate to design data solutions that adhere to FAIR principles

Provide oversight on data management deliverables to vendors

Manage timelines for successful study delivery

Review data using statistical tools to identify anomalies

Engage stakeholders to shape future initiatives

Job description

Responsibilities:

Collaborate with peers within function and across the organization to identify, design and execute fit for purpose data management solutions, ensuring FAIR principles are adhered to.
Acts as experts for data collection and acquisition, advising teams and stakeholders on best practices and proposing innovative solutions. e.g. the collection of new data types (e.g. emergent biomarkers), new technologies (e.g. sensors), and new data sources (e.g. EMR).
Partner with and provide oversight of data management deliverables (e.g. Task Ownership Matrix) to our Functional Service Providers (FSPs) and vendors. Provide Quality Assurance on tasks as applicable to ensure a high quality of data and compliance with applicable pharma industry regulations and standards.
Proactively manage timelines and track decisions, ensuring successful delivery of the study SOW carried out by FSP. Continue to be accountable for quality and where needed, provide business and technical expertise to our FSP.
Oversee sample management and eManifest process, ensuring timely, proactive resolution of queries.
Provide high level review of data using graphical visualizations, statistical monitoring tools, and standard metrics designed to identify outliers or trends in data received, gaps in standard checks programmed or other unexpected anomalies in the data.
As required, be the BDS DM representative on the SMTs and any associated meetings such as Data Quality meetings.
Proactively engage with stakeholders across the business and biometrics to understand their needs and influence their understanding of decisions made on our function.
Partner with relevant functions for external data vendor selection and management. Oversee development of data transfer agreements with vendors ensuring use of standards, fit for purpose data models and transfer intervals.
Contribute to functional, cross functional, enterprise-wide or external initiatives that shape our future landscape.

Requirements:

BS or MS in Life Sciences, Data/Computer Science, Bioinformatics, OR equivalent industry experience.
Demonstrated strong collaboration and excellent communication skills-both written and oral (proficiency in English required)
Knowledge of CDISC data standards
Knowledge of ICH-GCP and working in regulated environment
Project Management skills
Able to manage multiple requests and priorities.
Demonstrated leadership capabilities and decision-making, negotiation, motivation (self and others) and influencing.
Experience with data analytics and/or visualization tools and techniques
Demonstrated entrepreneurial mindset and self-direction, ability to mentor others and willingness to learn new techniques.
Knowledge of biological principles, display interest and demonstrate scientific curiosity including understanding of data types and their scientific use (clinical, biomarker, WGS, RNA-seq, etc
Experience in leading DM study teams and maintaining oversight of all start-up, conduct and close-out activities for multiple complex studies, ensuring fit for purpose quality.
Experience in leading the collection of clinical trial and/or Real World Data.
Good understanding of molecule and disease area strategies, healthcare environment, as well as strong scientific and technical expertise.
Extensive technical and or industry experience (for senior and principal roles)
Experience in enterprise level operating systems and familiarity with databases
Fluency in programming languages (SAS, R, Python, SQL, etc)
Some experience with advanced analytics approaches (e.g. machine learning, AI).
Experience with tools related to technologies required to undertake analyses on large data sources with computationally intensive steps (SQL, parallelization, Hadoop, Spark, etc)
Experience producing interactive outputs (e.g. Shiny, Tableau).
Contributor to open-source packages, libraries or functions.
Experience implementing reproducible research practices like version control and literate programmer.
Experience with SDTM implementation and CDISC standards.
Experience with standardized technologies such as MedDRA and WHO Drug.
BSN, RN, RPh, Pharm D, PA or other applicable health professional qualification preferred.

Required profile

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