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Rocket Learning

Data Scientist

Rocket Learning

Non-profit Organizations · 501-1,000 employees

20 h ago
Remote data-scientist Mid (2-5 yrs) Full-time
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About the role

Design, build, and deploy machine learning and LLM-based systems to drive personalization and improve educational programs. Manage the end-to-end data science lifecycle from problem definition to deployment and monitoring.

What they look for

Machine Learning LLM Deep Learning Python Pandas Numpy Scikit-learn PyTorch TensorFlow MLflow NLP Statistics Data Science RAG Embeddings

Requirements

Requires 3-6 years of experience in applied machine learning, NLP, or LLM development. Candidates must have strong fundamentals in statistics, ML algorithms, and proficiency in Python and relevant ML workflow tools.

Full description

This is a remote position.

Data Scientist (3-6 years of experience - Remote/Hybrid Bengaluru)

Design, build, and deploy ML and LLM-based systems that drive personalization, assist teachers, and improve programs.

What you’ll do

Develop, evaluate, and deploy ML, deep learning & LLM models

Own end-to-end DS lifecycle: problem definition → deployment → monitoring

Build reproducible, well-documented model workflows & pipelines

Implement strong model QA/QC: validation, drift detection, experiment logs

Collaborate with product & domain teams to shape modeling strategies

Continuously improve performance, documentation, reliability & processes

What we’re looking for

3–6 years in applied ML / ML engineering / NLP / LLM development

Strong fundamentals: statistics, ML algorithms, deep learning

Python (pandas, numpy, scikit-learn, PyTorch/TensorFlow)

ML workflow tools (MLflow, experiment tracking, registries)

Experience with LLMs, fine-tuning, embeddings, vector stores, RAG

Experience deploying models through APIs or containerized workflows

Key Success Factors

High ownership, clean engineering, excellent documentation

Excellent communication and collaboration across teams to translate needs into solutions

Curiosity, fundamentals, and bias for shipping reliable systems

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