Role Overview Mercor is working with a leading AI research lab on a project to improve how AI systems do real-world data science and data engineering work. We’re looking for senior data scientists and data engineers who have deep experience at top technology, quantitative or financial firms. You’ll use that expertise to train and evaluate frontier AI models. This role is open to candidates based in the United States, United Kingdom, Canada, Australia or New Zealand. Key Responsibilities
- Designing realistic, challenging data science and data engineering tasks, including but not limited to exploratory analyses, statistical modeling, machine learning pipelines, ETL and data pipeline design, SQL and Python workflows, and experimentation or A/B test write-ups
- Illustrating "what good looks like" by writing precise, task-specific criteria for data science and data engineering deliverables
- Applying consistent, evidence-based judgment so that assessments are reproducible and defensible
- Taking structured feedback from senior reviewers and iterating on submitted work
Ideal Profile
- 4+ years of professional data science, data engineering or machine learning experience in one of these settings:
- A leading technology or quantitative firm (such as FAANG+, Magnificent 7+, top-tier quant funds or equivalent) with a Mid-senior level title. Applied Scientist, Research Scientist and Machine Learning Engineer/Scientist roles also qualify.
- A top financial institution (a leading bank, hedge fund or trading firm)
- A top-tier consulting firm, working as a data scientist
- A Fortune 100 company, with a senior or manager-level title
- Experience may be split across more than one qualifying employer
- Strong command of Python, SQL, statistical modeling, machine learning, experimentation and causal inference
- Proven ability to turn messy real-world data into rigorous analyses and reliable pipelines
- Excellent written English, including the ability to explain technical findings clearly
- Bachelor's degree from an English-speaking country
- A detail-oriented, consistent approach to evaluating complex work
- Comfortable receiving feedback and calibrating judgment against set standards