Materials Science Analyst

Gramian Consulting

. Design scientific coding tasks with one main problem and at least 3 logically connected sub-problems .

Posted 9/21/2026contractRemote • Bangladesh, Brazil, Colombia, Egypt, Ghana, IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Python-based scientific computing, including designing and implementing scientific coding tasks with comprehensive unit testing. Proficient in quality control processes and optimizing tasks for AI evaluation criteria while ensuring scientific correctness.

Highest-signal resume keywords

Python-Based Scientific ComputingTask Design with Sub-ProblemsQuality Control Using Central Task Platform (CTP)Discriminative Test Case CreationOptimization Against Pass@K Criteria

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Python ProgrammingUnit TestingScientific ComputingTask DesignQuality Control ChecksTest Case DevelopmentOptimization TechniquesValidation of Scientific CorrectnessDeterminism AssessmentWell-Posedness Evaluation

Soft Skills

CollaborationFeedback ParticipationCommunication

Tools & Technologies

Central Task Platform (CTP)GPTGeminiNemotron

Industry Keywords

Materials ScienceScientific CodingAI-Generated OutputsQuality RubricsPass@K Evaluation

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Design scientific coding tasks with one main problem and at least 3 logically connected sub-problems
Implement verified Python solutions with complete unit test coverage
Create discriminative test cases that distinguish correct from incorrect AI-generated outputs
Perform quality control checks using the Central Task Platform (CTP), including Tier 1 structure checks and Tier 2 quality rubrics
Revise tasks and solutions based on QC feedback
Optimize tasks against Pass@K evaluation criteria across multiple LLM judges, including GPT, Gemini, and Nemotron
Validate scientific correctness, determinism, well-posedness, and expected outputs
Maintain a low rework rate and high first-submission quality
Participate in project reviews, feedback sessions, and standups during required overlap hours

Requirements

What you’ll need

Experience in materials science and scientific computing is required for the role's scope
Python-based scientific computing
Ability to design scientific coding tasks with one main problem and at least 3 logically connected sub-problems
Ability to implement verified Python solutions with complete unit test coverage
Ability to create discriminative test cases for AI-generated outputs
Experience performing quality control checks using the Central Task Platform (CTP), including Tier 1 structure checks and Tier 2 quality rubrics
Ability to optimize tasks against Pass@K evaluation criteria across GPT, Gemini, and Nemotron judges
Ability to validate scientific correctness, determinism, well-posedness, and expected outputs
Ability to participate in project reviews, feedback sessions, and standups during required overlap hours