Senior Scientist, Clinical Genomics Research, Hematology R & D
AstraZeneca is a global, science-driven biopharmaceutical company. We are dedicated to discovering, developing, and delivering innovative, meaningful medicines and healthcare solutions that enrich the lives of patients.
The vision of AstraZeneca Oncology is to redefine cancer, redefine our solutions to cancer, and restore patients' lives.
Within Hematology R&D , we are committed to advancing the science to deliver life-changing medicines to patients most in need.
With a combination-focused pipeline that exploits the power of six scientific platforms (Immuno- Oncology, Tumor Drivers and Resistance, DNA Damage Response, Antibody-drug Conjugates, Cell Therapy, and Epigenetics) to help address unmet clinical needs in a host of hematological cancers, we are motivated by a dedication to the scientific discovery and collaboration that will one day help eliminate cancer as a cause of death.
This unit has responsibility for the value chain from discovery through to late-stage development, enabling rapid acceleration of promising early-stage assets and life-cycle management programs.
The Translational Medicine function exists within Hematology R&D to ensure effective and strategic biomarker studies for Hematology & Oncology project teams.
Closely interacting with the pre-clinical Discovery, Early Clinical Development, and Precision Medicine & Biosamples diagnostics functions, the Hematology Translational Medicine team is the single point of accountability for delivery of robust, scientifically-driven biomarkers to all Hematology project teams, providing feasibility assessments, technical validation, centralized biomarker analysis capability for clinical samples, delivery of translational science research and early clinical phase engagement.
Main Duties and Responsibilities :
Data Analysis and Modeling : Engage in the development and refinement of data models to effectively analyze multimodal data.
Focus on establishing predictive and / or prognostic models with multimodal risk stratification. Critically assess and perform due diligence third-party data.
Strategic Decision Support : Contribute key insights from data-driven analysis to inform crucial go / no-go decisions and empower strategic planning.
Translate complex analyses into actionable recommendations helping steer the company’s direction.
Data Asset Development : Actively participate in generating and curating essential data assets, enhancing our analytical capabilities and supporting our research infrastructure.
Collaborative Research : Collaborate closely with clinical researchers in the design and execution of studies, providing expert analytical support for genomic, epigenomic, and RNA-sequencing data interpretation.
Effective Communication : Present findings to scientific, regulatory, and clinical stakeholders by translating complex concepts into detailed reports and presentations for diverse audiences.
Illustrate research findings and their practical implications in clear and concise ways. Positively contribute to the field of oncology via presentations at conferences and publications in high impact journals.
Required Skills :
- Demonstrated experience working with large multimodal datasets, including NGS data, and deriving / assessing clinical utility.
- Proficiency in statistical programming languages such as R or Python and development of interactive documents such as Shiny apps.
Familiarity with Unix and working with cloud infrastructure.
- Experience with machine learning frameworks and statistical modeling.
- Understanding in the use of statistical methods such as analysis of time-to-event data, particularly in the context of oncology clinical research.
Preferred :
- Experience in one or more of epigenomics, liquid biopsy, and MRD and related diagnostic assays.
- Training in biostatistics.
- Track record of interfacing with clinical stakeholders.
Minimum requirements :
- in a relevant field with 0 yrs exp or Master’s degree in a relevant field with 4+ years of relevant experience.
- Proven track record of handling, analyzing, and extracting valuable insights from large and complex datasets.
- Excellent communication and data presentation skills.
- Expertise in either R and / or Python programming and visualization.
- Familiarity with the UNIX environment and high-performance computing environments.
- Skilled in effective written and oral communication of complex data to non-experts and working within cross-disciplinary science teams.
- Capability of successfully managing multiple simultaneous projects.
- Enthusiasm and persistence in the application of analytical methods to complex biological problems.
- Experience contributing to the research community through publication, conferences, and code sharing.
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