Data Scientist

This role is for one of our clients
Industry: Software Development
Seniority level: Associate level
Min Experience: 1+ years
Location: Bengaluru
JobType: full-time
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
What You’ll Do
Fine-tune transformers and LLMs (LoRA/QLoRA) for entity extraction, classification and reranking
Extract entities from job descriptions and resumes, building prediction models, create knowledge graph and traversal models
Build retrieval, clustering and learning-to-rank pipelines over large taxonomies
Design evaluations, dig into errors, and ship models to production
Requirements
Hands-on applied NLP or ML experience: 1–3 years for Data Scientist I, 3–5 years for Data Scientist II
Strong Python, with PyTorch, Hugging Face Transformers, sentence-transformers, pandas and scikit-learn
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)
Solid grasp of embeddings, attention, contrastive training, and retrieval with bi-encoders and cross-encoders
Working knowledge of classical ML: gradient boosting, logistic regression, clustering, and dimensionality reduction
Comfort with statistics and evaluation design: sampling, significance, calibration and metric choice
SQL, plus the ability to explore data at the scale of millions of records
Clear written communication of experiments and results
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
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
Python, PyTorch, Hugging Face Transformers
Good-to-have skills
Neo4j, Cypher, pgvector

Work arrangement
Hybrid