Data Science Contractor 0926

The work

Experts evaluate and improve AI-generated responses across data analysis, statistical reasoning, machine learning, and applied modeling, build challenging and realistic data science problems used to train AI systems, and author expert-level, source-grounded work product. The work is hands-on: reading and writing analysis code is part of the job. It is long-form, self-directed, and fully remote.

Must-haves

• Five or more years of professional data science experience (analytics, machine learning, statistical modeling, data engineering, or similar applied work) in industry or research. Undergraduate study does not count toward the two-year minimum.

• Working proficiency in Python and/or SQL; comfort reading and writing analysis code is expected. Advanced degree in a quantitative field is a plus but optional; practical expertise outweighs credentials.

• Genuine availability for 30–40 hours/week across the full 12 weeks.

• Strong written communication; comfortable working independently and fully remote.

• General familiarity with AI/LLM tools (user-level is fine).

The strongest candidates look like

• Independent, consultant, and freelance data scientists. This is the strongest positive signal in our data, by a wide margin: self-employed and consulting profiles convert and retain dramatically better than any other data science profile, and the effect gets stronger among our very best performers.

• Small-company practitioners. Data scientists from small firms and startups substantially outperform their large-enterprise peers.

• Mid-career practitioners, roughly 5–10 years of experience. This band converts strongest; solid performers also appear across the 10–20 year range.

• Currently working candidates who control their own schedule. People with an active practice and real capacity reliably finish what they start.

About the Role

Our Client is seeking experienced data science professionals to join a 12-week AI training sprint as contract contributors. In this role, you will apply your domain expertise to evaluate, annotate, and improve AI-generated responses related to data analysis, statistical reasoning, machine learning concepts, and applied modeling — and to build challenging, realistic data science tasks used to train AI systems. Your work directly shapes the accuracy and reliability of AI tools used by data professionals every day. 

This is not a traditional data science role — no stakeholder decks, no production pipelines, no on-call. Instead, you will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment.

Key Responsibilities

• Review and evaluate AI-generated responses to data science prompts — analysis, modeling, statistics, and code — for correctness and rigor 

• Design realistic, challenging data science problems used to train AI systems

• Write clear, expert-level solutions and explanations that serve as model training examples 

• Flag errors, flawed reasoning, or ambiguous outputs and provide corrective guidance

• Run your tasks against AI models and iterate based on expert reviewer feedback

• Follow detailed written instructions precisely, including document and upload format requirements

• Maintain consistent quality and throughput throughout the sprint engagement

Qualifications 

• Minimum 2 years but ideally 5+ years of professional data science experience (analytics, machine learning, statistical modeling, data engineering, or similar applied work) in industry or research. Undergraduate study does not count toward the experience minimum. 

• Working proficiency in Python and/or SQL; comfort reading and writing analysis code is expected 

• Strong written communication skills; ability to explain complex concepts clearly and concisely 

• Comfortable working independently in a remote, self-directed environment 

• Hands-on practitioner: you currently do (or recently did) this work at a detailed, individual-contributor level — not solely in a managerial capacity

• Careful reader of written instructions; receptive to feedback and revision cycles

• Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing)

• Available to commit 30–40 hours per week for the full 12-week engagement 

• Based in the United States 

• Advanced degree in a quantitative field is a plus — practical expertise matters more than credentials 

Compensation & Terms 

• Pay is $125/hr 

• Paid on a regular cadence; contract position 

• Fully remote — work from anywhere in the US