Data Scientist – Customer retention
Sydney, Australia
Applications have closed
TAL
We offer flexibility by letting you tailor your cover to suit your individual needs. Quick and easy to apply. Get An Online Quote.Company Description
Welcome to This Australian Life.
From the millions of Australians we protect, to those that make it happen every day at TAL, people really are what we’re all about. We want to grow with you. Achieve with you. And support you to do your best work. That's why we're focused on developing leadership, promoting diversity, rewarding excellence and retaining great talent.
We're always looking for people who want to go further with us. People who do what’s right, aim high, and work smart. Why not see where we can go?
Job Description
As a Data Scientist focused on Customer Retention insights you will be working within the Actuarial Pricing and Value Team to support the management of retention and value across TAL's Retail advised and Direct lines of Life Insurance business. Your role will be to design, build as well as maintain our insights reporting, advanced analytics and data science capability to drive customer retention initiatives. This includes building and maintaining lapse and campaign reporting as well as predictive and machine learning models.
In this role you will:
- Manipulate and prepare existing data as input to drive critical lapse insights. This includes data cleaning, data wrangling as well as transformation and feature engineering using SQL, Python and/or R.
- Design end-to-end optimised data pipelines for data transformation.
- Create dashboards for data visualisation, monitoring and reporting including automated reports for continuous insights using Power BI and R.
- Perform ad-hoc data extraction and manipulation to produce analysis and present insights.
- Leverage both structured and unstructured data as modelling input to increase insight potential.
Qualifications
To be successful in this role you will have,
- A degree in one or more of these fields: mathematics, statistics, actuarial, data science/analytics, finance related discipline.
- 2+ years of industry experience.
- Advanced skills in wrangling data using SQL, developing data requirements, and running exploratory data analysis.
- Proven experience in building a range of analytical, statistical and machine learning models using SQL, Python, R.
- Experience in life or general insurance is desirable.
- A degree in actuarial studies and data science specific qualifications is desirable.
You’re always accountable for your actions. You never give up. You strive to find the best outcomes for customers and partners. And you value working together to find the best solutions for problems.
Additional Information
Work is a big part of this Australian life, and we work hard to make it one of the best parts. We don’t just say it; we do it. We offer a workplace that’s inclusive and flexible, supporting our people with options that let them make the most of their careers.
We know the value of having different people from all walks of life, with varied points of view and attributes regardless of their age, ethnicity, religion, sexual orientation, gender identity, intersex status or any disabilities they might be living with. We strive for a diverse and inclusive workplace where a sense of belonging encourages people to bring their full selves to work.
#LI-Hybrid
Everyone at TAL has a responsibility to do the right thing and is accountable for the way they conduct themselves. Our expectations are that you follow the principles set out in our Code of Conduct when you come to work every day. Risk management is everyone’s responsibility.
If you are already a TAL employee please apply via the SmartRecruiters button in Workday and navigate to the Employee Portal. This is important to ensure that your application is recorded accurately.
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Data analysis Data pipelines EDA Engineering Feature engineering Finance Machine Learning Mathematics ML models Pipelines Power BI Python R SQL Statistics Unstructured data
Perks/benefits: Flex hours
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