Data Scientist

AgilityIO is a software development firm that designs and builds custom applications for startups and Fortune 500 companies. We are a global team of over 400 developers, QA engineers, project managers, and UX/UI designers with offices in New York City and Vietnam.

This is a flexible, contract-based role open to candidates in Latin America.

We are seeking a Data Scientist to evaluate AI-generated data analyses on real datasets, stress-test the reasoning behind them, and write the judgments that teach the next generation of models what rigorous data work looks like. The newest models can spin up a full analysis in minutes — load the data, pick a method, produce charts and a confident conclusion. Someone has to check whether the statistics actually hold up, and that's this role.

• Evaluate AI-built analyses on real datasets: the method choice, the assumptions, and whether the conclusion actually follows from the numbers

• Red-team the statistics to expose leakage, p-hacking, confounded comparisons, and confident nonsense before real users trust them

• Write the better analysis when the model falls short: sound methodology, honest uncertainty, clear takeaways

• Build, train, and evaluate machine learning models (classification, regression, clustering, forecasting), including feature engineering, cross-validation, and metric selection

• Document methodology, assumptions, and limitations so that work can be reviewed and reproduced

• Collaborate with engineers, product managers, and other team members, and turn open-ended questions into well-defined analytical problems

• Professional, academic, or serious independent experience doing real data analysis in industry or research

• Proficiency in Python and its data stack (pandas, NumPy, SciPy, scikit-learn, statsmodels, or equivalents) and in SQL

• Experience working in Jupyter or similar notebook environments

• Experience with data visualization tools and libraries (matplotlib, seaborn, Plotly, or BI tools such as Tableau or Power BI)

• Working knowledge of machine learning fundamentals: model selection, overfitting, evaluation metrics, and validation strategies

• Clear written and spoken English

• No degree required

• Background as a Data Scientist, Data Analyst, or Analytics Engineer

• Experience reviewing, auditing, or red-teaming someone else's analysis or model output

• Familiarity with common pitfalls in applied statistics: p-hacking, leakage, confounding, multiple comparisons

• Familiarity with cloud platforms (AWS, GCP, or Azure), data warehouses (Snowflake, BigQuery, Redshift), and MLOps practices such as experiment tracking and model versioning

• Experience with Git and collaborative, version-controlled workflows

• Prior experience with AI/ML data annotation, evaluation, or model-training projects

• Skepticism and curiosity: you ask whether a result is real before you ask whether it is exciting

• Honesty about uncertainty: you communicate what the data can and cannot support

• Strong written communication and attention to detail

• Self-direction: you are comfortable working independently and asynchronously in a remote environment

• Competitive salary and performance-based bonuses

• Flexible remote work environment

• Professional growth opportunities and mentorship

• Engaging and collaborative team culture with cutting-edge projects

If you're passionate about rigorous data work and want to help shape how the next generation of AI models reason about data, we'd love to hear from you — send your resume to [email protected].