Data Science & ML Engineer (LatAm-based) at Tricura Insurance Group - a US-based healthcare startup specializing in risk management and claims innovation

Tricura Insurance Group is a rapidly growing company redefining the insurance landscape through data-driven insights and innovative technology. Founded by industry experts with backgrounds in clinical care, risk management, and technology, Tricura focuses on providing tailored liability coverage and advanced claims management for high-risk clients. Led by Matthew Queen (COO), Beau Walker (CTO), and Gabriel Mayer (CEO), the company leverages AI and machine learning to improve underwriting, risk analysis, and client outcomes. At Tricura, you’ll join a collaborative, fast-paced environment where innovation drives meaningful impact.

About the Role :

As a Machine Learning & Data Science Engineer , you’ll design, build, and deploy data-driven models that power Tricura’s core risk and claims intelligence.

This role is ideal for someone with a strong foundation in traditional data science and machine learning — from exploratory data analysis and feature engineering to model development, validation, and deployment. You’ll work closely with the CTO and partner with Data Engineering, Product, and Technical Leadership teams to turn complex data into actionable insights.

Key Responsibilities

1) Predictive & Statistical Modeling

Develop and refine models for risk prediction, claims forecasting, and cost optimization.

Apply classical ML algorithms (e.g., regression, random forests, gradient boosting, clustering, time-series forecasting) to structured and semi-structured data.

Conduct rigorous feature selection, model tuning, and validation using cross-validation and performance metrics.

2) Applied Machine Learning

Build, train, and deploy machine learning models that improve underwriting accuracy and risk assessment.

Collaborate with data engineers to optimize data pipelines and ensure clean, reliable data for modeling.

Translate model outputs into interpretable business insights and visualizations.

3) Natural Language & Unstructured Data

(Optional) Contribute to NLP projects where applicable, e.g., text classification or data extraction from claims documents.

Use classical NLP and embedding-based approaches before escalating to LLMs.

4) Production Deployment & Monitoring

Package models for deployment via APIs, Docker, or cloud-based solutions.

Continuously monitor model performance, retraining and refining as needed.

5) Cross-Functional Collaboration

Work with the CTO, Data Engineering, and Product teams to align model outputs with strategic goals.

Communicate complex analyses clearly to both technical and non-technical stakeholders.

Must-Have Requirements

Programming : Proficiency in Python.

Modeling & ML : Deep experience with frameworks like scikit-learn, statsmodels, PyTorch, or Keras.

Statistical Techniques : Strong grasp of regression, classification, time-series analysis, and clustering methods.

Data Engineering : Skilled in SQL (Postgres, MySQL, or Snowflake preferred), data wrangling, and feature engineering.

Cloud Platforms : Hands-on experience with AWS (S3, EC2, or similar).

Version Control : Proficiency with GitHub / GitLab.

End-to-End ML : Capable of building, tuning, interpreting, and deploying production-grade models.

Communication : Analytical mindset with the ability to tie technical results to business outcomes.

Nice to Have

Experience with R or other statistical analysis tools.

Background in healthcare, insurance, or finance.

Experience building underwriting algorithms or risk scoring models.

Familiarity with NLP or LLM-based tools (BERT, Llama, Claude, etc.) as a complement to traditional modeling.

What We Offer

Full-time position (8 hours / day, Monday–Friday) with flexible hours during EST (9 AM–5 PM).

Fully remote role — work from anywhere with a stable internet connection.

Long-term commitment (2+ years) with strong opportunities for professional growth.

Learning budget, mentoring, and ongoing performance feedback.

Compensation in USD, discussed during the interview.

Working hours :

8 hours a day, Monday to Friday, with flexible schedule during EST working hours (9 am to 5 pm) with daily team stand-ups at 12 : 30 PM EST. ( you may check the time difference via the link ).

Please note that the later you apply - the more intensive your selection process will be, for example, you will have fewer interview time slots to choose from, etc.

Selection Process :

Submit the application form with your CV & GitHub portfolio.

Zoom screening interview with a Hire5 recruiter.

Technical Zoom meeting with Tricura Insurance Group team.

Get hired!

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