# Competitive Data Science & Machine Learning Specialist (Kaggle Expert+)

**Company:** [Gramian Consulting Group](https://jobs.workable.com/companies/kANY7hHLXDH7fUqyifRLmf.md)
**Location:** Remote
**Workplace:** remote
**Employment type:** Contract
**Department:** Talent Solutions

[Apply for this job](https://jobs.workable.com/view/edcc7253-c743-494f-b072-f87dcb09a596)

## Description

**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 Job**  
We are looking for accomplished Competitive Data Scientists, Machine Learning Engineers, and Applied Mathematics Specialists with a proven track record in data science competitions, particularly on Kaggle and similar platforms.

This opportunity is designed for candidates who have achieved at least Kaggle Expert ranking, including Masters and Grandmasters, and can demonstrate strong practical skills in machine learning, statistical modeling, quantitative reasoning, and programming.

You will use your competition experience to design challenging, reproducible technical tasks that require genuine problem-solving rather than plausible-looking answers. Your work will involve building execution environments, implementing verified reference solutions, developing reliable evaluation criteria, and testing how AI Coding Agents perform against complex data science challenges.

A verifiable Kaggle profile with Expert, Master, or Grandmaster ranking is a key qualification. Strong practical coding and debugging abilities are essential. Previous AI training experience is not required.

-   **LOCATION:** Remote – US, Canada, Australia, New Zealand, UK
-   **WORKLOAD:** Approximately 15 hours per week.
-   **SCHEDULE:** Flexible; choose your working days and hours, including weekends.
-   **PROJECT:** AI training focused on competitive data science, machine learning, and reproducible technical problem-solving.
-   **AI EXPERIENCE:** Not required.

**Key Responsibilities**

-   Design challenging data science and machine learning tasks inspired by real-world problems and competitive data science scenarios.
    
-   Build reproducible execution environments, prepare datasets, configure dependencies, and implement verified reference solutions.
    
    Develop automated tests and evaluation criteria that distinguish correct solutions from incomplete, incorrect, or unreliable approaches.
    
-   Use coding agents to execute assigned tasks, investigate their failures, and evaluate their problem-solving capabilities.
    
-   Identify implementation errors, flawed assumptions, data leakage, statistical inconsistencies, and other issues affecting solution correctness.
    
-   Apply competitive data science techniques and mathematical reasoning to validate solutions.
    
-   Ensure tasks have clear requirements, reliable expected outputs, and reproducible verification procedures.
    
-   Refine task difficulty and evaluation methods based on observed coding-agent performance.
    
    Document task specifications, solution methodologies, technical reasoning, and verification steps.
    
-   Review and improve task quality to ensure scientific and computational accuracy.

## Requirements

-   Demonstrated experience in competitive data science, machine learning, quantitative modeling, or applied mathematics.
    
-   Kaggle profile with at least Expert ranking (Master or Grandmaster ranking strongly valued).
    
-   Ability to provide a verifiable Kaggle profile demonstrating competition achievements and ranking.
    
-   Strong understanding of machine learning, statistical modeling, data analysis, and quantitative problem-solving.
    
-   Practical programming ability, particularly in Python.
    
-   Ability to independently implement, test, debug, and verify data science solutions.
    
-   Experience working with datasets, computational environments, and relevant machine learning libraries.
    
-   Strong analytical reasoning and the ability to explain technical decisions.
    
-   Ability to identify data leakage, implementation flaws, and methodological errors.
    
-   Excellent written communication and technical documentation skills.
    
-   Professional English communication skills.