Tower Hill Insurance Group

Machine Learning Engineer

• Develop actionable insights and recommendations in support of business objectives using in-house and external data sources
• Collaborate with business and data engineering professionals for timely and reliable delivery of data solutions
• Leverage deep expertise in data engineering, data science, advanced analytics, and insurance industry data to aid business partners in risk management
• Drive efficiencies and improve customer experience
• Partner with business stakeholders to scope, structure, and drive analytical projects from requirements through delivery
• Present findings and recommendations clearly to both technical and non-technical audiences
• Source, validate, and integrate structured and unstructured data across platforms
• Conduct exploratory data analysis to reveal data quality issues and identify opportunities
• Analyze business trends, time series patterns, model performance, and causal relationships using advanced statistical and machine learning / deep learning methods
• Design, build, and deploy machine learning / deep learning models — including NLP, computer vision, and LLM-based solutions
• Create visualizations, dashboards, and reports that communicate analytical and model results to support decision-making across the organization
• Own and maintain recurring analytical, reporting, and model-monitoring processes
• Document data pipelines, model architecture, analytical frameworks, and deliverables for knowledge sharing across technical and non-technical teams

Machine Learning Scientist

Analytics Engineer

• Lead the end-to-end delivery of complex data engineering projects, including requirements definition with business stakeholders, technical design, implementation oversight, and delivery coordination with data engineering, analytics, and AI/ML teams
• Serve as the primary technical liaison between business partners and engineering teams, translating business requirements into well-scoped, executable data initiatives with clear outcomes
• Architect and develop cloud-native data solutions on AWS, including the design and deployment of data pipelines using AWS Glue, Airflow (Amazon MWAA), Step Functions, Lambda, and related services
• Lead the migration and modernization of existing data workflows, including the conversion of RPA-based automation into cloud-native orchestration solutions using AWS Airflow
• Design, develop, and deploy RESTful and authenticated APIs on Kubernetes (K8s) to expose data services and other reusable data products consumed by downstream systems
• Oversee and contribute to infrastructure-as-code practices using Terraform, ensuring analytics and data engineering workloads are provisioned, managed, and repeatable across environments
• Establish and enforce data pipeline best practices including data quality checks, lineage tracking, and governance standards across all projects under this role's scope
• Monitor, observe, and maintain production data systems and pipelines, using tools such as CloudWatch, DataDog, or Grafana as applicable
• Conduct exploratory and operational data analysis to surface deficiencies, validate pipeline outputs, and support data-driven decision making for business stakeholders
• Define and communicate data architecture standards and patterns to both technical and non-technical audiences, including documentation of architectural decisions and pipeline design
• Collaborate on cross-cutting concerns such as data security, infrastructure alignment, and platform strategy
• Coordinate with DevOps and infrastructure teams to ensure that data engineering deliverables align with CI/CD pipelines, containerization standards, and broader organizational engineering practices

Machine Learning Scientist

Principal Data Analytics Engineer