Our client, a technology-driven leader in the insurance software space, is seeking a Technical Lead - Data Science & Engineering to help architect and scale their unified data platform and Data-as-a-Service (DaaS) capabilities.
This is a hands-on leadership role ideal for someone who thrives at the intersection of data engineering, machine learning, and modern cloud infrastructure. You'll provide technical direction to a growing team of engineers and data scientists while collaborating with cross-functional stakeholders across product, engineering, and the wider business.
Key Responsibilities:
Lead the architecture and development of scalable data platforms and DaaS infrastructure (cloud & hybrid).
Define best practices and technical standards across data engineering and ML workflows.
Mentor and guide a multidisciplinary team, promoting robust CI/CD and monitoring strategies.
Oversee deployment and governance of ML models in production environments.
Collaborate on the design of secure, scalable data APIs for self-serve analytics.
Evaluate and introduce new tools and technologies to drive performance and scalability.
Required Experience:
Extensive background in data science, ML engineering, or data platform engineering.
Experience in a recent technical lead or architect-level role.
Proven delivery of large-scale data systems using cloud platforms (AWS, Azure, or GCP).
Deep knowledge of MLOps practices (MLflow, Docker, Kubernetes, etc.).
Demonstrated experience in building Data-as-a-Service (DaaS) solutions or data APIs.
Strong stakeholder engagement and mentoring skills.
Desirable:
Experience in insurance, financial services, or other regulated environments.
This is an exciting opportunity to lead high-impact data transformation in a company that values innovation, inclusion, and technical excellence.
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