Data Scientist

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

📋 Description

• Assess AI-generated analyses on actual datasets, reviewing method selection, assumptions, and the logical flow of conclusions drawn from the data.

• Conduct red-team evaluations of statistical methods to identify issues such as data leakage, p-hacking, confounding comparisons, and unwarranted conclusions.

• Compose refined analyses that utilize robust methodologies, transparently address uncertainties, and provide clear insights.

• Develop, train, and assess machine learning models for tasks such as classification, regression, clustering, and forecasting.

• Implement feature engineering, cross-validation techniques, and metric selection processes.

• Document methodologies, assumptions, and limitations to facilitate review and reproducibility.

• Collaborate with engineers, product managers, and other team members to achieve project goals.

• Transform open-ended inquiries into precisely defined analytical challenges.

⛳️ Requirements

• Proven professional, academic, or serious independent experience in conducting real data analysis within industry or research environments.

• Strong proficiency in Python and its data ecosystem (including pandas, NumPy, SciPy, scikit-learn, statsmodels, or similar) as well as SQL.

• Experience using Jupyter or comparable notebook environments.

• Familiarity with data visualization tools and libraries (such as matplotlib, seaborn, Plotly, or BI platforms like Tableau or Power BI).

• Understanding of machine learning principles, including model selection, overfitting, evaluation metrics, and validation techniques.

• Excellent command of written and spoken English.

• A formal degree is not required.

• Preferred: Experience as a Data Scientist, Data Analyst, or Analytics Engineer.

• Preferred: Background in reviewing, auditing, or red-teaming analyses or model outputs created by others.

• Preferred: Awareness of common issues in applied statistics, including p-hacking, leakage, confounding, and multiple comparisons.

• Preferred: Knowledge of cloud platforms (AWS, GCP, or Azure), data warehouses (Snowflake, BigQuery, Redshift), and MLOps practices like experiment tracking and model versioning.

• Preferred: Familiarity with Git and collaborative workflows that utilize version control.

• Preferred: Prior involvement in AI/ML data annotation, evaluation, or model training initiatives.

• Ability to work independently and effectively in a remote setting.

🏝️ Benefits

• Competitive salary along with performance-based bonuses.

• Flexible remote work options.

• Opportunities for professional development and mentoring.

• A dynamic and collaborative team environment engaged in innovative projects.