Primary DetailsTime Type: Full time
Worker Type: Employee
The DMML & Operational Insight Head plays a pivotal role in spearheading the conceptualization, development, and execution of innovative data engineering, machine learning, and operational insight solutions. This role serves as the driving force behind enabling strategic decision-making within the organization by harnessing the power of data-driven insights.
Collaborating seamlessly with diverse cross-functional teams, including adept data scientists, skilled software engineers, and insightful business analysts, the Lead not only comprehends intricate business requisites but also transforms them into robust technical solutions. Central to this responsibility is the assurance of the dependability, excellence, and security of the organization's data infrastructure and machine learning models. Guiding and nurturing junior team members is also a crucial facet of this role, cultivating a dynamic and high-performing team environment.
Remaining at the forefront of burgeoning trends and advancements in data engineering and machine learning is imperative. This awareness is skillfully woven into enhancing the organization's data solutions continually. Furthermore, the Lead is at the helm of strategizing and executing machine learning projects and models across the organization. This endeavor involves close collaboration with stakeholders to ensure that the implemented machine learning solutions drive tangible business value, fostering a culture where data-driven insights catalyze innovation and growth.
ROLE PURPOSE:
The DMML & Operational Insight Head plays a pivotal role in spearheading the conceptualization, development, and execution of innovative data engineering, machine learning, and operational insight solutions. This role serves as the driving force behind enabling strategic decision-making within the organization by harnessing the power of data-driven insights.
Collaborating seamlessly with diverse cross-functional teams, including adept data scientists, skilled software engineers, and insightful business analysts, the Lead not only comprehends intricate business requisites but also transforms them into robust technical solutions. Central to this responsibility is the assurance of the dependability, excellence, and security of the organization's data infrastructure and machine learning models. Guiding and nurturing junior team members is also a crucial facet of this role, cultivating a dynamic and high-performing team environment.
Remaining at the forefront of burgeoning trends and advancements in data engineering and machine learning is imperative. This awareness is skillfully woven into enhancing the organization's data solutions continually. Furthermore, the Lead is at the helm of strategizing and executing machine learning projects and models across the organization. This endeavor involves close collaboration with stakeholders to ensure that the implemented machine learning solutions drive tangible business value, fostering a culture where data-driven insights catalyze innovation and growth.
RESPONSIBILITIES
Strategic Leadership:
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Work closely with SVP - Transformation to develop and implement a comprehensive strategy for data engineering, machine learning, and operational insight initiatives aligned with the organization's goals.
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Define and communicate a clear roadmap for implementing data solutions and machine learning models.
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Work closely with SVP - Transformation to drive data monetization through innovative "as-a-service" models.
Solution Development:
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Collaborate with cross-functional teams to gather and comprehend business requirements and challenges.
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Design, develop, and oversee the implementation of data engineering and machine learning solutions that address these challenges effectively.
Data Infrastructure and Quality:
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Ensure the integrity, reliability, and security of the organization's data infrastructure and systems.
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Establish and maintain data quality standards, validation processes, and data governance practices.
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Team Leadership and Mentorship:
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Lead, mentor, and inspire a team of data scientists, engineers, and analysts, fostering their growth and professional development.
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Provide technical guidance and support to junior team members, promoting a culture of learning and innovation.
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Emerging Technologies and Trends:
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Stay abreast of industry trends, emerging technologies, and best practices in data engineering, machine learning, and operational insight.
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Integrate new knowledge into the organization's strategies to enhance data-driven decision-making.
Project Management:
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Lead the end-to-end execution of machine learning projects, from conceptualization to deployment.
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Define project scopes, deliverables, timelines, and resource allocation.
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Stakeholder Collaboration:
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Collaborate closely with business stakeholders, ensuring a clear understanding of their needs and priorities.
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Translate business requirements into actionable data and machine learning solutions.
Performance Monitoring and Optimization:
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Establish key performance indicators (KPIs) to measure the effectiveness of implemented solutions.
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Continuously monitor and optimize data solutions and machine learning models to improve accuracy and relevance.
Risk Management and Compliance:
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Identify and mitigate potential risks related to data privacy, security, and compliance with relevant regulations.
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Implement rigorous data governance and compliance practices, ensuring adherence to stringent regulatory requirements and implementing robust security measures to safeguard sensitive information throughout the data lifecycle.
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Implement appropriate safeguards to protect sensitive information.
Communication and Reporting:
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Communicate complex technical concepts to non-technical stakeholders, fostering a shared understanding of the value of data-driven insights.
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Prepare and present regular reports on the progress, impact, and outcomes of data engineering and machine learning initiatives.
Innovation and Continuous Improvement:
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Drive innovation by proposing and experimenting with novel approaches and technologies in data engineering and machine learning.
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Continually seek ways to optimize processes and enhance the efficiency of data solutions.
QUALIFICATIONS
Knowledge
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Strong understanding of data engineering and machine learning concepts, methodologies, and tools.
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Expertise in data warehousing, ETL processes, data modeling, and database design and management.
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Extensive experience in designing and implementing robust data pipelines utilizing Azure Data Factory and Azure Synapse Analysis, optimizing data integration, transformation, and analysis across the organization.
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Knowledge of big data technologies such as Hadoop, Spark, and Kafka.
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Familiarity with cloud-based data storage and processing technologies, such as AWS, GCP, or Azure.
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Experience working with machine learning frameworks such as TensorFlow, PyTorch, or Keras.
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Understanding of software engineering principles and practices, including version control, agile development, and continuous integration and deployment.
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Familiarity with data privacy, security, and governance regulations, and experience implementing security protocols and measures.
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Knowledge of statistical analysis and modeling techniques, and experience applying these techniques to solve business problems.
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Understanding of business processes and workflows, and the ability to translate business requirements into technical solutions.
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Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams and stakeholders.
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Proven ability to lead, motivate, and manage a diverse team of data engineers, data scientists, and analysts. Experience in setting clear expectations, providing regular feedback, and fostering a collaborative and innovative team culture.
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Skills
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Strong analytical and problem-solving skills, with the ability to quickly identify and solve complex data engineering and machine learning problems.
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Strong leadership and project management skills, with the ability to lead cross-functional teams and manage complex projects.
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Excellent communication and collaboration skills, with the ability to work effectively with both technical and non-technical stakeholders.
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Strong mentoring and coaching skills, with the ability to develop and mentor junior team members.
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Strong attention to detail, with the ability to ensure the quality, reliability, and security of the organization's data infrastructure and machine learning models.
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Proficiency in analyzing complex situations, making data-driven decisions, and effectively communicating the rationale behind those decisions to stakeholders.
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Strong organizational skills, with the ability to manage multiple projects and priorities simultaneously.
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Experience with agile development methodologies, and the ability to adapt quickly to changing priorities and requirements.
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Strong problem-solving skills, with the ability to think creatively and strategically to develop innovative solutions to business problems.
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Strong understanding of software engineering principles and practices, including version control, testing, and continuous integration and deployment.
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Strong understanding of data privacy, security, and governance regulations, and the ability to implement security protocols and measures.
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Experience
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Bachelor's or Master's degree in Computer Science, Data Science, or a related field.
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15+ years of experience in data engineering, machine learning, or a related field, with a proven track record of delivering high-quality, scalable data solutions preferably within the intricate landscape of the insurance industry.
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Experience leading cross-functional teams, managing complex projects, and mentoring junior team members.
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Experience working with big data technologies such as Hadoop, Spark, or Kafka, as well as cloud-based data storage and processing technologies such as AWS, GCP, or Azure.
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Experience working with machine learning frameworks such as TensorFlow, PyTorch, or Keras.
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Strong understanding of data warehousing, ETL processes, data modeling, and database design and management.
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Strong understanding of software engineering principles and practices, including version control, testing, and continuous integration and deployment.
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Strong understanding of data privacy, security, and governance regulations, and experience implementing security protocols and measures.
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Strong understanding of statistical analysis and modeling techniques, and experience applying these techniques to solve business problems.
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Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams and stakeholders.
How to Apply:
To submit your application, click "Apply" and follow the step by step process.
Equal Employment Opportunity:
QBE is an equal opportunity employer and is required to comply with equal employment opportunity legislation in each jurisdiction it operates.
Top Skills
What We Do
QBE Insurance Group is one of the world's top 20 general insurance and reinsurance companies, with operations in all the key insurance markets. QBE is listed on the Australian Securities Exchange and is headquartered in Sydney. We employ more than 11,000 people in 27 countries.
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