Summary
The Advanced Analytics team is responsible for utilising mathematics and machine learning methodologies to solve business problems, explore new opportunities and generate insights in collaboration with business units. Team members are data scientists and data engineers of varying experience with knowledge in one or more domain areas of data science, all of whom have a passion for using empirical research to solve complex problems and an ability to translate analytical insights into actionable recommendations for the business.
Here at Genworth, machine learning is considered atoolto solve business problems – the focus is solving the problem, not the machine learning itself. To understand the business and data, simple analyses, cross-tabulations, visualisations and generalised linear models are more widely used than complex machine learning. Typically, the Advanced Analytics team uses machine learning models as an intermediate step to feature reduce or engineer composite scores that are used as predictive factors in traditional statistical models. That said, the statistical models are complex, such as Markov chains, stochastic modelling, survival modelling and credit scoring.
Responsibilities
The main responsibilities include:
Designing and developing new data structures, as well as expanding existing data structures, that are required for Advanced Analytics and Actuarial modelling by:building a deep understanding of the business problems being solved by the Advanced Analytics and Actuarial modelling teams;
developing a thorough understanding of Genworth’s data assets;
building a solid relationship with the Enterprise Data Warehouse team;
utilising Genworth’s data technologies, such as Informatica, to design and develop data structures;
Adopting an iterative approach to initiatives, developing prototype solutions in Proof of Concepts (POCs) to test hypotheses, and embracing a fail-fast experimentation approach to solving complex problems;
Utlising machine learning techniques to feature reduce and engineer composite scores to be used as predictive factors in the Advanced Analytics models;
Becoming AWS Cloud Solution Architect certified and take responsibility for progressing the advanced analytics platform on AWS;
Deploying real-time statistical models and machine learning algorithms into a production operational environment using containerization;
Engineering solutions required for solving complex data problems such as web scraping and address cleansing;
Involvement in all phases of the data solution development lifecycle including requirements gathering and documentation, design, development, testing and deployment.
Communicating regularly and effectively with stakeholders throughout the entire development lifecycle.
Leveraging business and data knowledge to proactively pitch and discuss new ideas for generating data-driven insights with stakeholders and team members;
Championing the use of advanced analytics across the organisation through knowledge sharing and socialisation of advanced analytics techniques;
Ensuring all development follows the established advanced analytics standards, including Software Development Lifecycle (SDLC), model governance policies and audit requirements;
Adhering to the compliance obligations relevant to the position; perform duties in an ethical, lawful and safe manner; undertake training as directed by the Compliance Leader; report and escalate compliance concerns, issues and failures; and disclose potential conflicts of interest.
Most Frequent Contacts
The Advanced Analytics team is part of the Actuarial department in Finance. As well as providing mathematical and machine learning expertise into actuarial modelling, the team also works collaboratively with the following business units:
Risk;
Operations;
Information Technology (IT);
Commercial & New Ventures.
This role will communicate regularly with stakeholders from these business units throughout initiatives and proactively engage in discussions to pitch new ideas for insights.
Qualifications – Required
Degree in Computer Science/Engineering or equivalent;
5+ years in a data warehouse developer role using Informatica PowerCenter;
5+ years in a role utilising business analysis skills;
Experience in writing complex SQL/PLSQL queries to extract/interrogate datasets and building a data warehouse;
Ability to write scalable code to solve data engineering problems using Python programming language;
Sound understanding of data warehousing concepts and methodologies;
Good working experience of Oracle DB;
Solid knowledge of dimensional modelling;
Passion for using data and technology to solve complex business problems;
Ability to analyse and troubleshoot for complex issues;
Strong interpersonal and teamwork skills.
Qualifications - Preferred
Experience in using AWS Cloud machine learning and data lake services – AWS Cloud Solution Architect certification would be advantageous;
Ability to develop visualisations using Tableau software;
Ability to write scalable code for web scraping;
Familiarity with reading and analysing data from NoSQL databases – particularly data in JSON format
Experience in a previous role of deploying real-time statistical models and machine learning algorithms into a production operational environment or AWS Sagemaker using containerization;
Knowledge of statistical modelling techniques and machine learning algorithms – degree in Data Science would be advantageous;
Experience in insurance industry;
Econometric and/or housing and mortgage market knowledge.