Data & AI Strategy and Value Assurance, Senior Manager

Requirements

• Necessary Qualifications include:
• Postgraduate degree.
• Preferred Qualifications include:
• Bachelor's or Master's degree in Computer Science, Data Science, or a related field.
• Global Disclaimer:
• US Only Disclaimer:
• To successfully perform this job, the individual must be able to perform each essential job responsibility satisfactorily. Reasonable accommodations may be made to enable an in

Benefits

Additional Information

Primary Details Time Type: Full time Worker Type: Employee Data & AI Strategy and Value Assurance, Senior Manager Location: USA - New York Work Arrangement: Hybrid The salary range for this role is: $185,000-$278,000K The purpose of this role is to lead the development and implementation of data governance and management strategies within the organization, 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 conceptualization and execution of innovative data engineering, machine learning, and operational insight solutions to enable strategic decision-making and drive tangible business value across the organization. Your new role: Work closely with senior leadership to develop and implement a comprehensive strategy for data engineering and machine learning initiatives aligned with organizational 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. Work Experience: Necessary Work Experience includes: Significant relevant experience. Preferred Work Experience includes: 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.

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