Data Scientist I / II

Prismforce

Data Scientist

Prismforce

Job descriptionApplication

About the role

Data Scientist I / II | Prismforce | Bangalore (Hybrid) About Prismforce and the AI & ML team Prismforce is a Sequoia-backed enterprise SaaS company building the Talent Supply Chain platform for global technology, engineering and IT services firms, with clients across IT services, banking, healthcare and automotive.

The AI & ML team builds the intelligence layer underneath every product.That layer powers SkillPrism, SelectPrism and IntelliPrism, and our AI-readiness assessments for enterprise workforces. You will work on models that run in production for some of the largest employers in India and the US.

What you’ll do

• 1. Fine-tune transformers and LLMs (LoRA/QLoRA) for entity extraction, classification and reranking.
• 2. Extract entities from job descriptions and resumes, building prediction models, create knowledge graph and traversal models.
• 3. Build retrieval, clustering and learning-to-rank pipelines over large taxonomies.
• 4. Design evaluations, dig into errors, and ship models to production.

Requirements

• 1. Hands-on applied NLP or ML experience: 1–3 years for Data Scientist I, 3–5 years for Data Scientist II.
• 2. Strong Python, with PyTorch, Hugging Face Transformers, sentence-transformers, pandas and scikit-learn.
• 3. Has fine-tuned transformer models on their own data, such as an encoder for NER or classification, or a decoder LLM with PEFT (LoRA/QLoRA).
• 4. Solid grasp of embeddings, attention, contrastive training, and retrieval with bi-encoders and cross-encoders.
• 5. Working knowledge of classical ML: gradient boosting, logistic regression, clustering, and dimensionality reduction.
• 6. Comfort with statistics and evaluation design: sampling, significance, calibration and metric choice.
• 7. SQL, plus the ability to explore data at the scale of millions of records.
• 8. Clear written communication of experiments and results.
• 9. Experience with learning-to-rank, knowledge graphs or large taxonomies in a real product.
• 10. Experience with ML lifecycle tooling: experiment tracking, model versioning, Docker, and GPU training and serving on AWS.

• Nice to have

• Graph databases and graph ML: Neo4j and Cypher, pgvector, node embeddings, link prediction.

• Span-based or zero-shot NER (GLiNER, NuNER) and entity linking to a fixed ontology.

• Distilling LLM outputs into small models, and efficient serving with vLLM, quantisation or ONNX.

• Managed LLM platforms such as AWS Bedrock or SageMaker, and structured-output prompting at scale.

• Agent and workflow orchestration (LangGraph, n8n or similar) for multi-step curation pipelines.

• Ontology or taxonomy design, or prior work in HR tech, talent or skills data.

• Open-source contributions, Kaggle results or publications in NLP or IR.

• Why join

• Real scale: your models run on millions of enterprise data points.

• Breadth: you will touch extraction, graphs, ranking and LLMs, not one narrow slice.

• Ownership: a small, senior team where you ship and see the impact.

• Leadership from McKinsey, Deloitte, Amazon, Infosys, TCS and Uber, with an advisory board of former IT services CXOs.

• Perks and benefits

• Competitive compensation

• Group medical cover for self, spouse, children and parents

• Hybrid work and flexible policies

• Retirement benefits

• A self-driven career progression framework

• Must-have skills

• PythonPyTorchHugging Face Transformers

• Good-to-have skills

• Neo4jCypherpgvector

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