Associate Director/Scientific Leader Oncology Translation Big Data Mining

GSK, Plc.•Collegeville, PA

40d•Hybrid

About The Position

We are seeking a highly skilled and experienced individual to join our team as an Associate Director/Scientific Leader of Oncology Translation - Big Data Mining within the Oncology Translational Research Team. The successful candidate will have a key role in the analysis of large-scale cancer multiomic and clinical data, applying cutting edge algorithms and closely collaborating with AIML and project teams in supporting GSK oncology translational research. They will need a solid understanding of computational multiomic approaches, cancer biology and translational research to derive meaningful and reproducible biological insights that can help project decision making. Documented expertise in the analyses of multi-omic datasets (in solid and/or haematological malignancies) and interpretation of analysis outcomes in a translational setting is necessary whilst experience in complex molecular data integration will be advantageous. The successful candidate will be part of multidisciplinary teams to answer complex scientific/clinical questions and will need to have strong communication skills and the ability to convey complex ideas and concepts to a range of stakeholders, including peers and leaders. We create a place where people can grow, be their best, be safe, and feel welcome, valued and included. We offer a competitive salary, an annual bonus based on company performance, healthcare and wellbeing programmes, pension plan membership, and shares and savings programme. We embrace modern work practises; our Performance with Choice programme offers a hybrid working model, empowering you to find the optimal balance between remote and in-office work. Discover more about our company wide benefits and life at GSK on our webpage Life at GSK | GSK

Requirements

PhD or equivalent experience in data science, bioinformatics, computational biology, or a related field.
Considerable experience and proven record in cancer computational research (combined academic and or industry experience), including a deep understanding of computational approaches for large-scale multi-omic analysis.
Experience in cancer research and proven ability to apply computational methods in answering translational research questions and deriving clinically meaningful insights through integration of multi-omic data with clinical/phenotypic information.
Strong programming skills in Python, R, or similar languages.
Demonstrated ability to work collaboratively in multidisciplinary teams.
Excellent communication skills to convey complex data insights to diverse audiences.

Nice To Haves

Experience of contributing to translational research in a matrix team environment, with exposure to industry drug discovery and clinical development teams.
Evidence of independence in leading research projects and ability to apply cutting-edge statistical or bioinformatical methods.
Experience in complex molecular data integration including single cell data and spatial analyses.
Familiarity with machine learning and advanced statistical methods.
Knowledge of cloud computing tools and platforms (e.g., AWS, Google Cloud).
Excellent problem-solving and analytical skills to address complex scientific questions.
Strong collaboration and partnership skills to work effectively with cross-functional teams.
Proactive, 'can-do' mindset that bridges ideas to realizable and deliverable solutions to solve problems.
Highly organized with good attention to detail.
Sense of urgency, flexibility, integrity, and accountability.

Responsibilities

Process and analyse large scale somatic genomic datasets (e.g. WES/WGS, transcriptomics, proteomics) and combine clinical/phenotypic data to derive biological insights.
Integrate complex molecular data to derive insights and understand the underlying biology.
Lead multidisciplinary teams effectively to answer complex scientific questions with a clear view on clinical application.
Stay updated on emerging technologies and methodologies in big data analytics and apply them to ongoing projects.
Collaborate with cross-functional teams to support the development and implementation of oncology genomics strategies.
Present findings clearly and effectively to stakeholders, ensuring alignment with project goals.
Foster a culture of collaboration, innovation, and continuous learning within the team.
Ensure compliance with relevant regulations and guidelines in the field of oncology genomics.