Analyst II, Data Science

Location:
Bogotá, Colombia

About the Role
We are building a global Data Science hub in Colombia and seek an Analyst II. This hands-on role supports end-to-end model operations for global partners. It requires solid modeling experience, autonomy, and proven ability to deliver reproducible, production-ready solutions using modern MLOps practices. What you will do.
• Develop, validate, and operationalize predictive models (GLMs, tree-based, and other ML methods) to solve business problems across insurance areas.
• Work hands-on with large structured and unstructured datasets for feature engineering, model building, and diagnostics.
• Design, build, and deploy GenAI agents to support underwriting decisions, including risk assessment, document analysis, and policy recommendations.
• Apply prompt engineering, fine-tuning, and evaluation frameworks to ensure LLM reliability, accuracy, and compliance.
• Implement hallucination detection and guardrail mechanisms to ensure production-safe solutions.
• Design and run controlled experiments in Databricks (Databricks Experiments), track runs, analyze results, and promote reproducible workflows.
• Implement experiment and model versioning using GitHub for code/version control and experiment-tracking tools (MLflow or equivalent) for reproducibility.
• Collaborate closely with integral technical teams and business partners, delivering clear documentation, code reviews, and handoffs for production deployment.

Key Responsibilities
• Apply data science techniques to explore, manipulate, and analyze large structured and unstructured datasets to uncover insights and support business decisions.
• Apply traditional ML methods (GLMs, tree-based models)



and GenAI techniques to solve complex insurance problems. Partner with Underwriting & Claims stakeholders to gather requirements, define success criteria, and communicate AI capabilities in business terms.
• Build and maintain RAG pipelines, agent orchestration layers, and LLM evaluation frameworks for UW use cases.
• Implement monitoring for GenAI systems, including response quality, latency, and cost optimization.
• Translate complex quantitative findings into clear visualizations and compelling narratives for diverse audiences, including business stakeholders.
• Implement reproducible pipelines and adhere to software engineering best practices (version control, code reviews, unit tests, CI/CD where applicable).
• Maintain clear, business-focused communication with stakeholders, producing concise dashboards/reports and decision-ready insights.
• Stay current with Generative AI/LLM techniques and apply them where they add value.
• Engage with the global Data Science community, participating in cross-functional initiatives and knowledge-sharing forums to promote innovation and best practices. Skills and

Experience:
• Fluency in English C1 (Must)
• Bachelor’s degree in Statistics, Mathematics, Computer Science, Economics, Actuarial Science, or related quantitative field. Master’s preferred or equivalent industry experience.




• 5-6+ years of professional experience in data science, with proven experience building and validating predictive models.
• Proficiency in Python and SQL for data manipulation and modeling.
• Proficiency with Databricks and Databricks Experiments (running experiments, analyzing runs, reproducible code). Experience with Git for version control and collaborative workflows.
• Hands-on experience with AWS services (e.g., S3, EC2, and related) preferred.
• Demonstrated ability to work independently, manage multiple priorities, and deliver with minimal supervision.
• Strong analytical thinking, statistical rigor, and ability to explain complex quantitative results to non-technical stakeholders.
• High autonomy and ownership: able to scope and drive projects end-to-end.
• Strong communicator: able to present concise, business-focused arguments and recommendations. Proven skill in translating data science solutions into clear, measurable business actions and in communicating their value to stakeholders
• Collaborative and coachable: contributes to team practices and learns quickly from feedback. Highly desirable / nice to have
• Experience with AWS services (S3, EC2 or related)
• Experience implementing LLMOps pipelines, including response quality monitoring, cost tracking, and automated retraining
• Knowledge of responsible AI frameworks, including fairness, explainability, and regulatory compliance in AI-assisted decisions
• Familiarity with structured output generation and function/tool calling for agent reliability
• Knowledge of actuarial/insurance modeling concepts and domain experience.

📌 Analyst II, Data Science (Bogotá)
🏢 Liberty Specialty Markets Latam
📍 Bogotá