We need data scientists to review code structures, verify statistical analyses, and ensure data integrity.
Specialized data science tasks
Pipeline Audit
Review Python data scripts, pandas manipulation operations, and sklearn pipelines.
Statistical Verification
Audit models on hypothesis testing, p-values, regression techniques, and A/B test results.
How it works
Diagnostic Test
Verify your credentials and complete a 15-minute diagnostic test in Data Science.
Choose Projects
After diagnostic verification, browse open workspaces on your calibration center.
Weekly Payments
Conduct quality reviews, keep track of task approvals, and transfer payments weekly.
Typical workflow preview
Review models' attempts, point out factual or code-logic issues, and submit a corrected text prompt back to the frontier workspace.
import pandas as pd
result = df.groupby('category')['sales'].agg(['mean', 'std']).reset_index()Verified agg functions and correct output dataframe columns shape.
Where AI models need you most
Frontier LLMs frequently breakdown during subtle reasoning and technical validation. These represent key focus vectors.
Pandas/NumPy manipulation
Checking indexing logic, vectorization, and data shapes.
SQL Optimization
Verifying joins, aggregations, window functions, and subqueries.
ML Concepts
Checking explanations of bias-variance, overfitting, and ML metrics.
Statistical models
Verifying distributions, ANOVA, and regression assumptions.