Role directory

Data science jobs

6 active, referral-verified opportunities.

Math / Remote

Data Science Expert

1. Role Overview — Mercor is partnering with a leading AI research organization to engage experienced data scientists for a project focused on evaluating how well AI systems perform real-world data science work. Rather than producing deliverables yourself, you will define what excellent work looks like: designing task-specific grading criteria and scoring completed work samples with rigorous, well-reasoned written justifications. — 2. Key Responsibilities • Design precise, task-specific grading criteria for real-world data science deliverables (analyses, models, dashboards, experiment readouts, and written recommendations) • Score AI-generated and human work samples against those criteria, with detailed written justifications for every score • Apply consistent, evidence-based judgment so that scores are reproducible and defensible • Incorporate structured feedback from senior reviewers and iterate quickly on your work — 3. Ideal Qualifications • 5+ years of professional data science experience in industry • Background in business operations, product, or growth data science at top-tier technology companies • Deep fluency in experiment design and A/B testing, metric definition, SQL/Python analysis, and communicating findings to executive stakeholders • Exceptionally strong written communication • Detail-oriented, consistent, and comfortable having your judgment reviewed and calibrated against peers • Prior experience with AI training, evaluation, or human-data projects is a strong plus — 4. Application Process • Submit your resume or relevant technical background to get started • Qualified applicants may be asked to complete a brief technical assessment or submit additional information

$120 - $170 / hourOpen / Referral verified
Code / Remote

Data Scientist Talent Network

Mercor is building a network of experienced data scientists for potential future projects with leading AI research organizations. These projects may focus on evaluating how effectively AI systems perform real-world data science work. — There is no immediate project opening, but qualified applicants may be contacted as relevant opportunities become available. — 2. Potential Responsibilities — Future projects may involve: • Designing precise, task-specific grading criteria for data science deliverables, including exploratory data analyses, statistical modeling work, machine learning pipelines, experimentation and A/B test write-ups, feature engineering, and technical reports or notebooks • Evaluating AI-generated or human-created work against established criteria • Providing detailed written justifications for evaluations and scores • Applying consistent, evidence-based judgment so that assessments are reproducible and defensible • Incorporating structured feedback from senior reviewers and iterating on submitted work — Specific responsibilities will vary depending on the project. — 3. Ideal Qualifications • 1+ years of professional data science experience • Experience at a leading technology, research, or quantitative firm (such as top FAANG, AI labs, top-tier quant funds, or equivalent) • Strong command of Python, SQL, statistical modeling, machine learning, experimentation and causal inference, and translating messy real-world data into rigorous analyses • Exceptional written communication skills, including the ability to convey technical findings clearly • A detail-oriented and consistent approach to evaluating complex work • Comfort receiving feedback and calibrating judgment against established standards

$100 - $150 / hourOpen / Referral verified
Code / Remote

Frontend Engineer — Web Replication Preference Rater

About the work — We're building a high-quality dataset of human preference judgments on AI-generated frontend code. You'll be shown a reference web page alongside two candidate replications produced by AI models, and you'll decide which replication is better — then explain why in writing that a model can learn from. — This is evaluation work, not authoring. You won't be building sites from scratch. You'll be reading someone else's HTML and CSS, running it locally, comparing it pixel-by-pixel against a target, and articulating exactly where and why it falls short. — What you'll do • Render a reference page and two candidate replications side by side at desktop width and judge which is the closer reproduction. • Diff layout fidelity in detail: box model and spacing, typography (family, size, weight, line-height, letter-spacing), color and border treatment, image and asset handling, z-order and overflow. • Inspect the underlying markup with browser devtools to distinguish a replication that is genuinely correct from one that merely looks correct at one viewport — hardcoded pixel offsets, absolute positioning standing in for real layout, and inline styles that will not survive a resize. • Evaluate responsive behavior and semantic quality: whether flexbox and grid are used where they belong, whether legacy float or table layouts in the reference were reproduced faithfully, whether headings and landmarks carry real semantic meaning. • Write a structured rationale for every judgment — the specific defects you found, ranked by how much they matter, in language precise enough to be actionable. • Flag ties, ambiguous cases, and broken task items rather than forcing a preference. — You're a fit if you have • 3+ years of professional web development experience, primarily in frontend or full-stack work. • Fluency in hand-written HTML and CSS: semantic markup, flexbox, grid, media queries, and older float- and table-based layouts you can still read and reason about. • Working command of browser devtools — element inspection, computed styles, the box model, and the network panel. • Enough JavaScript to read a page's scripts and understand what they do to the DOM, even if you don't write JS daily. • Comfort in a terminal: cloning a folder and serving it over a local static server without help. • Strong written English and the discipline to justify a judgment rather than assert it. — Equipment • A desktop or laptop with a browser window that opens to at least 1920px wide. • Administrator rights on your own machine, so you can install and run a local server. — Nice to have • Prior RLHF, preference labeling, or model evaluation work. • A code review or technical assessment background. • Pixel-perfect design-to-code experience — translating Figma or PSD comps into production markup. • Web accessibility expertise (WCAG, ARIA, screen reader testing). • Familiarity with how LLMs typically fail at codegen. • Web scraping or DOM parsing experience. — Note: this seat is for practicing web developers. Backend-only, mobile-native-only, data science, DevOps, and design-without-code backgrounds are out of scope for this project.

$90 / hourOpen / Referral verified
Math / United States Remote

Data Science & Quantitative Analysis Expert

Join a leading AI lab's cutting-edge GenAI team to be at the core of the AI revolution, where your expertise fuels the development of the most advanced AI models. — 1. Overview — A leading AI lab is building the next generation of agentic evaluation benchmarks for frontier models and needs experienced data scientists and quantitative analysts to act as ground-truth experts. You will design complex analysis tasks that simulate real research work — for example, comparing two anomaly-detection algorithms on a dataset, calculating correlations, performing manual spot checks, and summarizing the findings in a notebook clear enough to drive a researcher's decision. — Each task represents one to two days of continuous, focused effort and spans multiple skills: data cleaning, statistical analysis, interpretation, and clear reporting. You will work in a tight feedback loop with the lab's researchers, verifying exactly where and why frontier models fall short on rigorous analytical work. — This is a full-time W-2 employment position with Cincinnatus LLC, with the opportunity to be placed at a leading AI lab as part of their extended workforce. This role is fully remote within the United States, at approximately 35 hours per week. — 2. Key Responsibilities • Design tasks: Create realistic data-analysis challenges — cleaning messy data, comparing methods, interpreting results — based on the kind of analysis you do every day. • Author notebooks: Work through your own tasks in Jupyter or Colab, producing clear, reproducible reference analyses. • Compare methods: Build tasks that ask for a fair comparison between analytical approaches, backed by spot checks and a clear recommendation. • Evaluate models: Review how models handle your tasks, and check whether their statistics and conclusions actually hold up. • Work as a team: Compare notes with researchers and fellow experts to keep evaluations consistent and accurate. — 3. Core Qualifications • MSc or PhD in statistics, data science, or another quantitative STEM field, or equivalent practical experience in a research-heavy analytical domain. • 1+ years of experience in a research, research-engineering, or heavy data-analysis role. • Deep hands-on data-analysis skills: data cleaning, statistical correlation, hypothesis testing, and careful interpretation of results. • Proficiency with Jupyter Notebooks or Google Colab for analysis and reporting. • Working proficiency in Python (pandas, NumPy, or similar) and Git. • Strong ability to communicate analytical findings in writing for decision-makers. • Past experience in AI training, model evaluation, or benchmark/task authoring is preferred. • A perfectionist mindset: high attention to detail, creativity in task design, and the ability to work independently through ambiguous, open-ended problems. • Ability to engage reliably for approximately 35 hours per week. — About Cincinnatus LLC — Cincinnatus LLC is an enterprise staffing company that partners with leading technology companies to source and employ highly skilled professionals for contingent and contract-based opportunities. Cincinnatus serves as the employer of record for these engagements, providing W-2 employment, payroll, benefits, and compliance, while placing employees directly within client teams to work on high-impact initiatives. — Roles hired through Cincinnatus are not project-based or freelance engagements. They are structured, role-based positions that typically involve part-time or full-time commitments, close collaboration with a client's internal teams, and integration into standard enterprise workflows. — Cincinnatus is a legal entity separate from Mercor. While opportunities may be discovered through Mercor's platform, employment, onboarding, payroll, and benefits for these roles are administered by Cincinnatus LLC. — Equal Employment Opportunity — Cincinnatus is proud to be an Equal Employment Opportunity employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or any other legally protected characteristic. — Cincinnatus is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans throughout the job application process.

$60 - $90 / hourOpen / Referral verified
Code / Remote

Network Engineer - Data for Autonomous Systems annotation

Are you a Level 3 / Tier 3 network support engineer interested in data science and autonomous infrastructure? Our client is building vertically integrated networking systems and using the data they generate to power the next generation of AI-driven infrastructure. They're looking for engineers experienced in final escalations, packet analysis, troubleshooting, and RCA workflows to help label, annotate, and structure networking data from real production systems. — This is a hands-on role that blends your L3 troubleshooting and incident-response experience with a growing understanding of how data pipelines are built and used in AI systems. — In this role, you'll: • Review real-world data from deployed networks: logs, configs, telemetry, event streams • Label and classify network behaviors, issues, anomalies, and incident patterns • Help define schemas and structure for large-scale data pipelines that downstream ML models will train on — You're a strong fit if you: • Work today as a Level 3 / Tier 3 / Principal Support Engineer keeping existing enterprise infrastructure online and stable — on-call rotation, RCAs, final escalations, troubleshooting outages — Must Have • Have hands-on experience with end-customer enterprise networks (switches, APs, firewalls in retail, healthcare, financial, manufacturing, university, hospitality, etc.) — Must Have • Bring hands-on Wi-Fi/wireless proficiency — enterprise WLAN controllers (Cisco WLC, Aruba, or Meraki), 802.1X/RADIUS, and wireless troubleshooting — Must Have • Do packet-level troubleshooting yourself — Wireshark, tcpdump, SPAN captures • Are curious about how raw infra data becomes machine learning input — This is a maintainer role — likely not the right fit if your current work is mainly network design/architecture, cloud/SRE, security/SOC, or IT helpdesk. — Your work will directly feed into the pipelines that power client's AI models, and help shape how intelligent systems reason about networks in the real world. — Here are more details about the role: • You will interface directly with the client team. • You are expected to work 30-40 hours/week, with your hours overlapping the Pacific (PT) business day. • This is an individual 1099 contract paid to a personal account — no corp-to-corp or agency billing. • You must be authorized to work in the US or Canada without sponsorship.

$50 - $70 / hourOpen / Referral verified
Math / Remote

Data Science and Analytics Experts

Role Overview • Mercor is seeking senior data science and analytics professionals to build evaluation tasks for AI systems operating in Fortune 500 enterprise data and analytics contexts. • The workflows are calibrated to the data scale, model complexity, and business-critical stakes of Fortune 500 and large public company data operations. • Contributors design enterprise data science scenarios, draft reference outputs, and write rubrics that capture how senior F500 data leaders think. — Key Responsibilities • Construct enterprise data science scenarios spanning large-scale predictive modeling, multi-stakeholder analytics governance, and complex data infrastructure decisions at F500 accounts. • Build analytics tasks across F500 machine learning model development, enterprise data pipelines, business intelligence at scale, experimentation/causal inference, and data strategy. • Develop data and MLOps scenarios involving tools such as Snowflake, Databricks, Python/R, SQL, Tableau/Power BI, and enterprise ML platforms (SageMaker, Vertex AI, MLflow) in F500 stacks. • Apply enterprise data science methodologies (statistical rigor, A/B testing frameworks, model validation, MLOps best practices) and produce reference analyses, model documentation, and executive-level insights. • Author rubrics that distinguish authentic enterprise data science judgment from generic textbook or tutorial-level recall. — Ideal Qualifications • 5+ years working as a data scientist, analytics leader, or ML engineer at a Fortune 500 technology or enterprise organization (Google, Meta, Amazon, Microsoft, Netflix) or inside an F500 data/analytics organization (JPMorgan, UPS, Unilever, PepsiCo, Walmart). • Direct ownership of F500 data products, F500 analytics initiatives, or F500 machine learning systems in production. • Fluency in enterprise data science tooling and methodologies, plus understanding of how F500 data governance, privacy compliance, and cross-functional stakeholder alignment actually work. • Prior rubric, technical curriculum, or model documentation authorship is a plus.

$60 - $70 / hourOpen / Referral verified