Data Scientist
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At a glance
Job overview
Rocket Learning is hiring a Data Scientist. 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 program outcomes. The role emphasizes end‑to‑end model development, rigorous quality control, and strong collaboration with product and domain teams.
Key focus areas include Develop, evaluate, and deploy ML, deep learning, and LLM models, Own end-to-end data science lifecycle from problem definition to deployment and monitoring, and Build reproducible, well-documented model workflows and pipelines.
Important skills include Python, Pandas, NumPy, scikit-learn, PyTorch, and TensorFlow.
Skills & qualifications
Skills
Qualifications
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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