Temporary Data Scientist

Data Scientist
- (Machine Learning & Databricks) Salary | $2,000 USD/month (Bi-weekly payments) Location | Remote from Latin America Schedule | Full-time Employment Type | Contractor S. holidays off, generous PTO, internal training, and mentorship Latino Legends is partnering with a high-growth U.
Our client does not just report on data—they build with it, driving marketing decisions for over 1,000 dealer clients across digital and direct channels. They operate on a cloud-native Databricks Lakehouse platform and foster an AI-forward culture where modern tooling accelerates analysis and deployment. As a Data Scientist, you will work primarily inside Databricks building datasets, features, and machine learning models that power marketing intelligence. You will spend your time on data preparation, exploratory analysis, feature engineering, and model training/evaluation, supported by senior engineers and architects on the production side. Data & Modeling (80%) Build and maintain data pipelines and transformations in Databricks using Python, PySpark, and SQL. Perform exploratory data analysis to understand marketing, campaign, and customer datasets. Engineer features, prepare training datasets, and train, evaluate, and tune machine learning models. Support model deployment and monitoring in production alongside senior engineers. Investigate and resolve data quality issues in source feeds and pipelines. Leverage AI coding tools efficiently while maintaining code quality and deep understanding. Collaboration & Quality (20%) Participate in sprint planning, standups, and retrospectives within an Agile/SCRUM framework. Write tests, data validation checks, and participate in code reviews.

Experience:
1-3 years of hands-on experience in data science, machine learning, or analytics engineering (including internships, co-ops, research, or complex projects). Degree in Computer Science, Statistics, Mathematics, Engineering, Data Science, or comparable practical experience. Solid proficiency with Python (pandas, NumPy, scikit-learn) and SQL (joins, aggregations, window functions). Data Science Fundamentals: Solid understanding of supervised learning, train/test splits, feature engineering, cross-validation, and evaluation metrics (ROC AUC, RMSE, precision, recall). Statistics & Data Handling: Working knowledge of descriptive statistics, hypothesis testing, data profiling, and data cleaning. Modern Workflow: Proficiency with Git, collaborative development, and AI-assisted development tools (Cursor, GitHub Copilot, Claude, etc.). Strong written and spoken English skills to effectively explain complex findings to non-technical audiences. Hands-on experience with Databricks (Unity Catalog, clusters, jobs, notebooks) or PySpark. Exposure to MLflow, Delta Lake (medallion architecture), or Azure cloud services. Familiarity with visualization tools (Power BI, Tableau, Plotly) or CI/CD pipelines (GitHub Actions, Azure DevOps). Why Apply Through Latino Legends? Mentorship & Career Growth: Work side-by-side with senior architects who review your code and help you transition from notebook experiments to production-level machine learning.