Job Description
Turning data into insight is a skill that demands both rigour and intuition. Chegg is recruiting experienced data scientists and analytics professionals to help our AI systems develop that same discipline. As an SME, you will design evaluation tasks, build reference solutions, and scrutinise AI-generated analyses so our models learn to reason about data the way a seasoned practitioner would. The work is fully remote, asynchronous, and task-based.
Core Responsibilities
- Design data science challenges covering statistical modelling, exploratory analysis, machine learning pipelines, and business intelligence interpretation
- Build authoritative reference solutions — with correct methodology, appropriate statistical tests, and clear data visualisations — against which AI outputs are measured
- Review AI-generated analyses, dashboards, and model outputs for methodological correctness, valid assumptions, and honest interpretation of results
- Identify flawed statistical reasoning, misleading conclusions, or incorrect applications of machine-learning techniques in AI-produced content
- Document error patterns systematically and deliver structured feedback that the research team can act on without ambiguity
Key Qualifications
- Degree in Data Science, Statistics, Computer Science, Mathematics, or a related quantitative discipline — Master’s or PhD strongly preferred; relevant project experience is a plus but not required
- Proficient in Python or R for analysis and modelling; hands-on with SQL and at least one BI tool such as Tableau, Power BI, or Looker
- Sharp eye for methodological weakness — can detect and explain flawed statistical arguments in plain English
- Self-directed; organised; comfortable delivering quality work asynchronously
Nice to Have
- Background in deep learning, NLP, or reinforcement learning frameworks
- Domain expertise in healthcare, financial, or operational analytics
- Experience with cloud-based data platforms such as AWS, GCP, or Azure
Why Chegg
- Fully remote and flexible — no fixed schedule
- Task-based commitment, typically 10–40 hours per week
- Real-world quantitative challenges with direct AI impact
- Pathway to ongoing projects for high-quality contributors