Data Science Task Designer

About Turing:

Based in San Francisco, California, Turing is the world’s leading research accelerator for frontier AI labs and a trusted partner for global enterprises deploying advanced AI systems. Turing supports customers in two ways: first, by accelerating frontier research with high-quality data, advanced training pipelines, plus top AI researchers who specialize in coding, reasoning, STEM, multilinguality, multimodality, and agents; and second, by applying that expertise to help enterprises transform AI from proof of concept into proprietary intelligence with systems that perform reliably, deliver measurable impact, and drive lasting results on the P&L

Role Overview:

The DS Agents project focuses on designing real-world, data-driven analytical tasks that evaluate how advanced Large Language Models (LLMs) reason over messy, realistic datasets. Unlike toy problems or clean benchmark datasets, these tasks intentionally reflect the kinds of ambiguity, noise, edge cases, and multi-table reasoning encountered in real business, scientific, and operational environments.

The goal is to stress-test data understanding, analytical reasoning, and decision-making capabilities of AI systems in a way that closely mirrors how human data scientists work in practice.

Responsibilities:

Contributors will work with one or more real datasets (CSV files) spanning diverse domains. Each task requires carefully crafting a natural, real-world analytical question that an AI agent must answer by interpreting the data.
2.1 Domain Coverage
Tasks will span multiple domains including but not limited to:
Business Analytics
Customer segmentation, sales trend analysis, churn prediction insights, market basket analysis, revenue attribution
Finance
Risk assessment, fraud detection patterns, portfolio analysis, transaction anomalies, credit scoring factors
Healthcare
Patient outcome analysis, treatment effectiveness, resource utilization, readmission patterns, clinical trial insights
Human Resources
Employee attrition analysis, performance correlations, hiring funnel metrics, compensation benchmarking, workforce planning
Supply Chain
Inventory optimization, demand forecasting, supplier performance, logistics efficiency, lead time analysis
IT Operations
System performance analysis, incident pattern detection, capacity planning, service level compliance, security log analysis
Scientific Research
Experimental data interpretation, statistical significance analysis, correlation discovery, hypothesis validation, outlier investigation
2.2 Task Complexity
Many tasks will involve:

• Multi-table reasoning: Combining information across multiple CSV files with different schemas
• Handling inconsistencies: Working with missing values, duplicate entries, and conflicting data
• Outlier management: Identifying and reasoning about anomalous data points
• Non-trivial reasoning: Going beyond simple aggregations to derive meaningful insights
• Ambiguity resolution: Making reasonable assumptions when data is incomplete or ambiguous
  
  Required Skills
• Strong data science fundamentals: Solid understanding of data analysis, statistics, and exploratory data analysis techniques
• Analytical reasoning: Ability to break down complex questions into logical steps and derive insights from data
• Edge case thinking: Natural inclination to consider what could go wrong, what edge cases exist, and how to handle ambiguity
• Data wrangling experience: Comfortable working with imperfect, messy, real-world datasets
• Precise reasoning: Ability to clearly distinguish between correct and incorrect answers with objective criteria

Relevant Background

Ideal candidates will have experience in one or more of the following:

• Data Science or Analytics roles
• Business Intelligence or Data Engineering
• Machine Learning or Applied Research
• Quantitative Analysis (Finance, Operations Research, Economics)
• Academic research involving data analysis
• Quality Assurance or Test Design for data systems

Technical Proficiency

• Proficiency in SQL and/or Python/Pandas for data manipulation
• Experience working with CSV, Excel, and tabular data formats
• Understanding of relational data models and join operations
• Familiarity with statistical concepts and metrics

Perks of Freelancing With Turing:

• Work in a fully remote environment.
• Opportunity to work on cutting-edge AI projects with leading LLM companies.
• Potential for contract extension based on performance and project needs.

Offer Details:

• Commitments Required : At least 4 hours per day and minimum 20 hours per week with overlap of 4 hours with PST. (We have 3 options of time commitment: 20 hrs/week, 30 hrs/week or 40 hrs/week)
• Engagement type : Contractor assignment/freelancer (no medical/paid leave)
• Duration of contract : 1.5 months; [expected start date is next week]