Data Analytics Solutions Consultant
Job Summary
We are seeking an Analytics Technology Solutions Lead to design, build, and deploy production-grade analytics applications that influence insurance deal cycles. This role involves bridging the gap between brokerage requirements and technical implementation by building Python Dash applications and managing data pipelines. You will collaborate with actuaries and data scientists to operationalize models and enhance operational efficiency.
Responsibilities
- Design and lead full-stack development of analytics applications
- Build intuitive broker-facing UIs and client deliverables
- Operationalize statistical and actuarial models
- Develop and maintain scalable data pipelines
- Collaborate with stakeholders to translate business workflows into technical specs
Required Skills
- Expert Python/Dash development
- SQL and Cloud data platforms (Databricks/Azure)
- Commercial insurance domain knowledge
- Statistical and actuarial modeling
- CI/CD and API development
Job Details
### Job Overview
We are looking for an Analytics Technology Solutions Lead (Data & Analytics Solutions Consultant) who builds things that influence insurance deal cycles. This is not a mere data engineering or infrastructure role. You will design, build, and deploy production-grade analytics applications that support placement decisions, benchmark limits and pricing, and give clients a clearer view of their risk across Commercial Lines and Employee Benefits.
### Key Duties
- Design and lead full-stack development of analytics applications using Python, Dash, and Fast API hosted on Databricks and Azure.
- Build broker-facing UIs that are intuitive and produce client-ready deliverables with limited analyst intervention.
- Operationalize statistical and actuarial models for loss trend analysis, frequency-severity decomposition, and program pricing validation.
- Develop and maintain data pipelines and adapters that ingest, normalize, and quality-check data from clients, carriers, and third-party sources.
- Apply CI/CD practices across pipeline and application deployments while maintaining governance and auditability standards.
- Collaborate with data engineering partners on scalable architecture for benchmarking and modeling workloads.
- Partner with Business Analysts to translate brokerage workflows into application requirements and data architecture decisions.
- Engage with brokers and client executives to understand the value of analytics in deal cycles.
- Create adoption plans, training materials, and feedback mechanisms for every tool shipped.
### Required Qualifications
- 7+ years in the insurance industry or a data-intensive financial services environment.
- Bachelor’s degree in computer science or quantitative engineering (Master's preferred).
- Expert-level proficiency in Python/Dash for application development, statistical modeling, and API development.
- Deep SQL expertise with relational and columnar environments.
- Experience with cloud platforms (Databricks, Azure) for managing CI/CD pipelines.
- Working knowledge of commercial lines insurance brokerage workflows and actuarial concepts.
- Ability to present technical work to non-technical audiences (brokers, practice leaders, and client risk managers).