Materials Science Analyst

Gramian Consulting

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

Posted 9/21/2026full-timeRemote • Pakistan, Indonesia, Kenya, Nigeria, Turkey, VietnamMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Python-based scientific computing and materials science, with a strong focus on designing and implementing scientific coding tasks. Proficient in quality control processes and optimizing tasks for AI evaluation criteria.

Highest-signal resume keywords

Python-Based Scientific ComputingMaterials Science ExpertiseQuality Control Checks Using CTPTask Optimization Against Pass@K CriteriaDesigning Scientific Coding Tasks

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Python ProgrammingUnit TestingTest Case CreationScientific ValidationTask Design

Soft Skills

CollaborationFeedback Participation

Tools & Technologies

Central Task Platform (CTP)GPTGeminiNemotron

Industry Keywords

Quality ControlDeterminismWell-PosednessPass@K Evaluation Criteria

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

Materials science expertise
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 distinguishing correct from incorrect 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 maintain a low rework rate and high first-submission quality
Ability to participate in project reviews, feedback sessions, and standups during required overlap hours