Data Scientist / Statistical Modeller
This ad is now closed and is an example of a job including maths skills.
Our client is an Australian market leader in supplying Data Analytics and Insights based solutions to the country’s leading consumer based businesses. Due to the ongoing success of the business a brand new position has been created for a Data Scientist / Statistical Modeller to join the Sydney based team. With the key focus on delivering client driven predictive modelling projects the responsibilities of this role will include:
- Engaging with internal and external stakeholders to consult around analytical project requirements, discuss methodologies and negotiate deliverables.
- Supplementing client’s consumer data with extensive data assets to uncover compelling insights into customer acquisition, retention and growths strategy (requires SQL, SAS, R or Python skills)
- Deliver a range of bespoke analytics projects leveraging techniques that may include; Customer propensity/regression modelling (churn, acquisition, cross sell), segmentation analysis (cluster), marketing campaign analytics, hypothesis testing, credit risk analysis, credit scorecard development, price optimization, customer lifetime value analysis, next best offer analysis, decision trees, neural networks, etc. (requires SQL, SAS R or Python analyst skills)
The successful applicant will come from a strong background working in a data driven insights environment and will be able to demonstrate:
- Commercial experience building a range of statistical models relating to customer marketing strategy (Acquisition, retention, growth) or credit risk analytics (scorecard development and validation)
- Strong technical skills and experience using SQL, SAS, R or Python to extract, manipulate and merge large customer behavioural or transactional data set from a variety of source systems (e.g. SQL Teradata, Oracle SQL, SQL Server, MYSQL, SAS Base, Python or R)
- Educated to a minimum of degree level in an analytical discipline such as – Mathematics, Statistics, Econometrics, Actuarial Studies, Data Science, Computer Science etc.
- A genuine passion to build a career in Data Analytics / Data Science, implementing analytical techniques and concepts such as; Customer propensity modeling (churn, acquisition, cross sell), segmentation analysis (cluster), marketing campaign analytics, hypothesis testing, credit risk analysis, credit scorecard development, price optimization, customer lifetime value analysis, next best offer analysis, decision trees, neural networks, etc. (requires SQL, SAS, Python or R analyst skills).