Data Scientist

Weekday; Bengaluru; Sur place; INR 2 000 000 - 7 000 000; Plein temps

Avantages offerts par ce poste

Competitive compensation

Group medical cover for self, spouse,/

Hybrid work and flexible policies

Retirement benefits

Self-driven career progression

Résumé du poste

Weekday is hiring for an NLP/ML Engineer role in Bengaluru. You will refine transformers and LLMs, build systems to extract entities from job descriptions and resumes, and create knowledge graphs.

You will also design retrieval and ranking pipelines for large taxonomies and deploy models to production. The ideal candidate has 1–5 years of hands-on NLP/ML experience, strong Python, PyTorch, and ML tooling, and familiarity with PEFT methods and knowledge graphs.

Qualifications

• Hands-on NLP/ML: 1–3 yrs for Data Scientist I, 3–5 yrs for Data Scientist II.
• Strong Python with PyTorch, Hugging Face Transformers, sentence-transformers, pandas, scikit-learn.
• Fine-tuned transformer models on data (NER/classification) or decoder LLM with PEFT (LoRA/QLoRA).
• Solid grasp of embeddings, attention, contrastive training, retrieval with bi-/cross-encoders.
• Classical ML knowledge: gradient boosting, logistic regression, clustering, dimensionality reduction.
• Statistics and evaluation design: sampling, significance, calibration, metrics.

Responsabilités

• Fine-tune transformers and LLMs (LoRA/QLoRA) for extraction and ranking.
• Extract entities from job descriptions and resumes; build prediction models and knowledge graphs.
• Build retrieval, clustering and learning-to-rank pipelines over large taxonomies.
• Design evaluations, analyze errors and deploy models to production.

Connaissances

Python

PyTorch

Hugging Face

sentence-transformers

pandas

scikit-learn

SQL

Outils

Neo4j

Cypher

pgvector

Docker

AWS

GPU training

Description du poste

This role is for one of our clients

Industry: Software Development

Seniority level: Associate level

Min Experience: 1+ years
Location: Bengaluru
JobType: full-time

₹20,00,000 - ₹70,00,000 a year

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.

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