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 global 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 integral Data Science community, participating in cross-functional initiatives and knowledge-sharing forums to promote innovation and best practices.

Skills and Experience:

Fluency in English and Spanish (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

📌 Analyst II, Data Science (Colombia)
🏢 Empresa reconocida
📍 Colombia