Senior Data Scientist

This is a fully remote position, open to applicants in Brazil.

📋 Description

• Design and implement data science experiments, which include causal analysis, A/B testing, and offline evaluations.

• Create, assess, and refine predictive models focused on credit/risk scoring, revenue forecasting, and policy performance.

• Take ownership of model performance and monitoring: establish success metrics, investigate data drift, and enhance data quality and feature reliability.

• Collaborate with Product Engineering to deploy models and analytics, prioritizing reliability, reproducibility, and maintainability.

• Conduct exploratory data analysis, feature engineering, and thorough validation on complex, real-world data.

• Clearly convey insights and recommendations to both technical and non-technical stakeholders through comprehensive documentation and presentations.

• Elevate analytical standards, code review processes, and documentation to improve technical quality.

• Mentor and guide team members through collaborative work, constructive feedback, and sharing of best practices.

⛳️ Requirements

• Over 5 years of professional experience in data science, applied machine learning, or a similar quantitative role.

• Solid understanding of statistics and experimentation, including hypothesis testing, causal reasoning, and evaluation design.

• Demonstrated experience in developing and deploying predictive models (classification, regression, time series) and assessing their real-world impact.

• Strong skills in Python and SQL, with a comfort level working with production data workflows.

• Experience in defining success metrics, collaborating with stakeholders, and delivering comprehensive outcomes.

• Excellent written communication abilities and a pragmatic approach suitable for fast-paced environments.

• Experience in managing model performance, monitoring for drift, and enhancing feature reliability.

• Nice to Have

• Experience in credit risk, underwriting, fraud/risk signals, or financial forecasting.

• Familiarity with modern data tools and warehouses like BigQuery or Snowflake, and transformation frameworks such as dbt.

• Understanding of MLOps practices (model deployment, monitoring, feature stores, orchestration) and cloud environments.

• Experience handling messy third-party data sources (banking data, eCommerce platforms, marketing signals).

🏝️ Benefits

• Competitive salary and performance-based bonuses.

• Comprehensive health, dental, and vision insurance.

• Flexible work hours and remote work options.

• Opportunities for professional development and continuous learning.

• Collaborative and inclusive company culture.