Data Scientist at Hays Specialist Recruitment (Adelaide, Australia)

Your new company

PointData is an Australian owned and based predictive analytics company that provides superior property and planning advice based on state of the art algorithms driven by artificial intelligence. Their aim is to better inform people’s decisions, help business grow and assist policy makers to shape our cities. The company includes a strong management team, high calibre data scientists and systems engineers with extensive modelling experience, advised by property and planning professionals with Australia-wide residential property and development, urban planning and spatial data experience. PointData is on an exciting growth journey and is now seeking an experienced Data Scientist to support their ongoing expansion plans.

Your new role 

This is a key position for the business and a newly created role to support growth plans. You will be working alongside another established Data Scientist reporting into the CIO. The tech team pride themselves on being collaborative and working together to achieve goals. Core duties will include;

Assist the development of new products and intellectual assets via data science –

• Formulate and lead analytical studies against large datasets
• Data cleaning/filtering as necessary
• Interpretation and analysis of data via statistical approaches
• Design, implementation & validation of machine learning and other predictive models
• Design and implement experimental processes to explore datasets and data models
• Deliver commercial outcomes

What you'll need to succeed

• Experience in design and implementation of data structures and databases
• Experience in design and implementation of data collection, conversion and processing pipelines
• Experience in software development with a focus on Python projects
• Experience in feature implementation & maintenance of existing projects
• Experience in development and implementation of statistical methods for analysis and validation
• Experience in development and implementation of predictive numerical models