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Data Scientist

Rocket Learning

Remote · location unlistedJobPosted 1mo agoStill listed 5 days ago

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At a glance

Compensation
No compensation found
Location
Remote · location unlisted
Work Authorization
Not specified

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Job overview

Rocket Learning seeks a remote Data Scientist to design, build, and deploy machine learning and large language model systems that personalize education, assist teachers, and enhance programs. The role involves end‑to‑end data science lifecycle ownership, rigorous model QA/QC, and collaboration with product and domain teams to continuously improve performance, documentation, and reliability.

Skills & qualifications

RequiredNice to have

Skills

Machine LearningDeep LearningNatural Language Processing SystemsStatisticsAPI DevelopmentContainerizationCollaborationPythonPandasNumPyScikit‑LearnPyTorchTensorFlowMLflowExperiment TrackingModel RegistriesLLMsFine‑TuningEmbeddingsVector StoresRAGMachine Learning AlgorithmsCommunication

Qualifications

3–6 Years Applied ML Experience

Full job description

Job 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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