About Gramian
Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs.
About the Role
We are looking for Data Scientists and Quantitative Professionals to help evaluate and improve next-generation AI models. You will assess AI-generated solutions to problems involving statistics, machine learning, experimentation, and quantitative reasoning. You will also create challenging prompts, datasets, and verified reference solutions while identifying methodological flaws, statistical errors, and weaknesses in analytical reasoning. This role is ideal for candidates with relevant professional experience in technology, finance, or research who can communicate technical concepts clearly.
SENIORITY: Junior / Early-Career Professional
LANGUAGE: Strong written English
LOCATION: Remote — English-speaking countries
Key Responsibilities
Evaluate AI-generated outputs across data science, statistics, machine learning, experimentation, and quantitative problem-solving.
Create expert-level prompts, datasets, and reference solutions for AI training and evaluation.
Identify flawed methodologies, statistical inaccuracies, unsupported assumptions, and weaknesses in quantitative reasoning.
Assess the correctness, reliability, and clarity of analytical approaches and conclusions.
Provide structured, evidence-based feedback to help improve AI model capabilities.
Apply consistent evaluation criteria and communicate technical findings clearly.
Requirements
Evaluate and enhance next-generation AI models by assessing AI-generated solutions and identifying methodological flaws in quantitative reasoning.
Build and maintain statistical, predictive, and causal analytics for local government datasets, turning community trends into clear insights while automating data workflows and supporting client projects.
Process and model client data during onboarding, develop automated statistical and machine-learning solutions for commercial decisions, and support operational requests with rapid analysis and scripting.
Process and model client data during onboarding, build solutions for the Buynomics ML application, and help develop automated statistical and machine-learning methods for commercial decision-making.
Maintain and improve data science learning content by analyzing learner feedback, debugging exercises, updating workspaces and cloud labs, and keeping technical examples accurate and aligned with current tools.
