Data Analytics Manager
Employment Info
JOB DETAILS
Requirements
- Strong general insurance domain knowledge, ideally commercial insurance, workers’ compensation or general insurance actuarial.
- Deep knowledge of the full data lifecycle with hands-on experience building data capture, data pipelines, reporting and statistical/ML models end-to-end.
- Knowledge of data and analysis languages: SQL, SAS, Python, R, PL/SQL, PG/PL, VBA or Java.
- Uses AI regularly and appropriately to accelerate delivery utilization across coding assistants for pipelines and queries, drafting documentation, speeding up analysis and prototyping models.
- Can evaluate and integrate AI/ML capabilities into insurance workflows (e.g. risk scoring, document extraction, anomaly detection) and assess output for quality, bias and compliance.
- Practical knowledge of business intelligence and reporting platforms such as Tableau, Power BI, SAS Visual Analytics, Qlik or Hex.
- Practical knowledge of data platforms, warehouses and cloud tooling e.g. Snowflake, Databricks, dbt, Airflow, Postgres, Redshift, Microsoft SQL Server, Oracle or DB2, AWS, Google Cloud Platform or Azure and Git/GitHub.
- Practical knowledge of data and risk regulation such as the Australian Privacy Principles (APP), GDPR, along with practical knowledge of ASIC / APRA standards (e.g. CPS 230, CPG 234/235), and an understanding of SOC 2 and ISO 27001 and frameworks and controls that satisfy them.
- Practical knowledge of governance, engineering, analytics/reporting, data science (AI/ML), architecture and automation.
Responsibilities
- Support the delivery of the end-to-end data lifecycle – capture, storage, transformation, modelling, materialisation and insight. Establishing the foundations the insurance business runs on.
- Be hands-on in the delivery of data assets and products across data capture, pipelines, reporting and analytical/statistical models. Collaborating with business and technical subject matter experts to deliver best in class assets and products.
- Support the selection of, and development of a fit for purpose data architecture and tooling (cloud platform, warehouse, orchestration and business intelligence).
- Support the delivery of a fit for purpose data governance and controls framework aligned to applicable regulation so data is trusted, compliant and audit-ready.
- Translate underwriting, pricing, claims and operational questions into analysis and models that measurably improve decisions across the business.
- Collaborate with business subject matter experts to build out the reporting, semantic layer and self-serve insight layer so leaders and operators across SafetyCulture Care can answer their own questions.
- Embed artificial intelligence and machine learning (AI/ML) into core insurance workflows in partnership with actuarial, underwriting, product and engineering.
- Support the growth of a geographically and diverse skilled data team across governance, engineering, analytics, data science and automation.
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