Employer : Melanoma Institute Australia

Closing date:   24/12/2024

Brief position description:  

The Personalised Immunotherapy Program (PIP) is a 5-year program which applies machine learning- based models to identify patients with cancer that is likely to be unresponsive to anti-cancer immunotherapies. PIP aims to ensure each patient’s cancer is screened for genetic, transcriptomic, and immune profiles to identify patients unlikely to benefit from standard therapies. The team has a wealth of multi-omics datasets and is currently deploying predictive models into oncology clinicals in real-time.

The PIP team is seeking a full time experienced Computational Data Scientist with a strong analytical background to leverage the vast resources available to monitor and improve the accuracy of melanoma diagnosis, prognosis, and suitability of anti-cancer therapies. The position entails deploying machine learning models to identify patient responses to therapies using clinical and multi-omics data sets and overseeing the long-term accuracy of the predictive models. The candidate will also build on these models with new approaches and additional information from novel technologies to improve patient selection for systemic therapies.

The data scientist will be responsible for the implementation of predictive models into a biomarker program within oncology clinics. The role will work alongside multiple clinical and research teams to interrogate multi-omics datasets, imaging data and other resources available at MIA. This role will require the candidate to have excellent computational skills, preferred experience in the cancer setting, and a passion to improve cancer patient outcomes.

The successful candidate will be part of the PIP team that resides within the Translational Research Laboratory of the Melanoma Institute Australia, based at The University of Sydney Camperdown campus. 

Key Selection Criteria
To be successful in this role you will have:
- PhD in data science, computational biology or related field
- Demonstrated skills in computer languages (eg –Python, R)
- Experience in processing and analysing multi-omics and/or imaging datasets.
- Experience in deploying machine learning models
- Scientific track record in developing machine learning models
- Experience in the clinical integration of multi-omics datasets
- Knowledge in deploying predictive models in the clinical setting
- Excellent project management skills with the ability to multitask.
- Preferred working knowledge of cancer biology and/or immunology
- Preferred interest in spatial biology and emerging technologies.

This is a fixed term position until December 2026 with view to a permanent role upon completion of fixed term contract.

Job website:   http://www.seek.com.au/job/80431713?tracking=SHR-WEB-SharedJob-anz-1

Contact name: Ann Michelle Arrabaca

Contact email: Careers@melanoma.org.au