Data & AI Strategy and Value Assurance, Senior Manager

QBE Insurance Group•New York, NY

2d•Onsite

About The Position

The purpose of this role is to lead the development and implementation of data governance and management strategies within the organisation, aligning closely with the Group Data Strategy and Governance team. This role will drive the establishment of a data-driven culture, oversee the coordination of divisional and group activities to enhance data management and mitigate business risks by addressing data issues. Additionally, the role involves spearheading the conceptualisation and execution of innovative data engineering, machine learning, and operational insight solutions to enable strategic decision-making and drive tangible business value across the organisation.

Requirements

Postgraduate degree.
Significant relevant experience.
Analytical Thinking
Business Data Analytics
Coaching for success
Collaboration Tools
Commercial Acumen
Critical Thinking
Data Quality Assessments
Influencing
Intentional collaboration
Managing performance
Navigating ambiguity
Risk Management
Stakeholder Management
Strategic Management
Strategic Planning

Nice To Haves

Experience in data management, warehousing, and leadership roles within insurance, financial, or consulting services firms, with a successful track record of implementing impactful data strategy, standards, and quality programs.
Solid working experience in defining and implementing data quality, metadata, and master data.
Sound experience in shared services, BPO, or financial services, including leadership, change management, analytics, and outsourcing technical experience.
Experience in managing large teams and complex/diverse accounts.
Experience in reviewing and analysing requirements and performing business impact analysis.
Experience in data engineering, machine learning, or a related field, with a proven track record of delivering high-quality, scalable data solutions within the insurance industry.
Experience leading cross-functional teams, managing complex projects, and mentoring junior team members.
Experience working with big data technologies such as Hadoop, Spark, or Kafka, as well as cloud-based data storage and processing technologies like AWS, GCP, or Azure.
Experience working with machine learning frameworks such as TensorFlow, PyTorch, or Keras.
Strong understanding of data warehousing, ETL processes, data modelling, and database design and management.
Strong understanding of software engineering principles and practices, including version control, testing, and continuous integration and deployment.
Strong understanding of data privacy, security, and governance regulations, with experience implementing security protocols and measures.
Strong understanding of statistical analysis and modelling techniques, with experience applying these techniques to solve business problems.
Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams and stakeholders.
Bachelor's or Master's degree in Computer Science, Data Science, or a related field.

Responsibilities

Work closely with senior leadership to develop and implement a comprehensive strategy for data engineering and machine learning initiatives aligned with organisational goals.
Define and communicate a clear roadmap for implementing data solutions and machine learning models.
Lead, mentor, and inspire a team of data scientists, engineers, and analysts, fostering their growth and professional development.
Ensure the integrity, reliability, and security of the organisation's data infrastructure and systems.
Establish and maintain data quality standards, validation processes, and data governance practices.
Translate business requirements into actionable data and machine learning solutions.
Drive innovation by proposing and experimenting with novel approaches and technologies in data engineering and machine learning.
Communicate complex technical concepts to non-technical stakeholders, fostering a shared understanding of the value of data-driven insights.
Prepare and present regular reports on the progress, impact, and outcomes of data engineering and machine learning initiatives.
Continuously monitor and optimise data solutions and machine learning models to improve accuracy and relevance.