Scientific Analyst for Mental Health Research

1 week ago

Sydney, New South Wales, Australia beBeeDataScience Full time $86,511 - $96,863
Job Description

We are seeking a highly skilled Data Scientist to contribute to our team of researchers and clinicians working on innovative mental health projects. The successful candidate will apply modern data science methods to drive data architecture, processing pipelines, and analytic methods for raw passive mobile sensor streams and wearable time-series data.

The role involves working with diverse data sources, conducting data wrangling, and contributing to the development of novel methodologies to model illness trajectories in youth mental health. Additionally, the candidate will collaborate with the wider team to develop ideas for grant applications.

Required Skills and Qualifications
  • Minimum 2 years experience in mathematics, statistics, and/or data science roles or related fields
  • Fluency in R or other modern data-science languages such as Python
  • Experience in wrangling data from multimodal and disparate sources into formats for statistical analysis
  • Experience processing high-frequency sensor or digital phenotyping data at scale
  • Knowledge in machine learning and longitudinal modeling, preferably applied to the field of mental health
  • Experience dealing with real-world datasets, including heterogeneous structured data with missing or unreliable fields
What We Offer

The successful candidate will have the opportunity to work collaboratively as part of a multidisciplinary team, applying their skills and expertise to drive meaningful research outcomes. With a passion for the application of data science to youth mental health, you will be part of a dynamic and innovative team committed to improving mental health services for young people in Australia.

About You

To succeed in this role, you will possess excellent communication skills, organisational and interpersonal skills, as well as the ability to work independently and complete tasks on time with minimal supervision.

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