Data Scientist

Weekday; Bengaluru; On-site; INR 3,000,000 - 5,000,000; Full time

Job summary

Weekday is seeking a Data Scientist I/II to design and deploy production-grade ML/NLP solutions in a scalable environment. You will develop entity extraction, semantic retrieval, and knowledge graph applications on large enterprise datasets, with emphasis on transformer-based models and MLOps.

The role requires hands-on Python, PyTorch, and model fine-tuning experience, plus comfort with cloud platforms and Docker-based workflows. Join a fast-moving AI team across product and data engineering.

Qualifications

• 1+ years of experience in applied ML, NLP, or data science.
• Proficient Python with PyTorch, transformers, pandas, and scikit-learn.
• Experience fine-tuning transformer models on custom datasets.
• Experience with PEFT/LoRA/QLoRA for efficient fine-tuning.
• Strong understanding of embeddings, semantic search, and information retrieval.
• Experience with SQL and analyzing large-scale datasets.
• Familiarity with Docker and cloud environments (AWS).
• Exposure to knowledge graphs, entity linking, or large taxonomies is a plus.

Responsibilities

• Fine-tune transformer models and LLMs using techniques such as LoRA and QLoRA for extraction, classification, and reranking.
• Develop NLP models to extract, classify, and link entities from resumes, job descriptions, and unstructured data.
• Build knowledge graphs and graph traversal solutions representing skills, roles, and competencies.
• Design semantic retrieval, clustering, and learning-to-rank pipelines across large datasets.
• Develop embedding-based search using bi-encoders and cross-encoders; implement retrieval pipelines.
• Apply classical ML methods (gradient boosting, logistic regression, clustering).
• Design evaluation frameworks, select metrics, and validate model performance.
• Deploy, monitor, and improve models in production; use Docker and cloud infra.

Skills

Python

PyTorch

Transformers

NLP

SQL

Docker

AWS

Knowledge retrieval

Tools

Hugging Face

Neo4j

pgvector

ONNX

SageMaker

Job description

This role is for one of our clients

Industry: Software Development

Seniority level: Associate level

Experience: 1+ yrs

Location: Bangalore

Job Type: Full-time

₹30,00,000 - ₹50,00,000 a year

We are looking for a talented and technically strongData Scientist I / IIto develop and deploy production-grade machine learning, Natural Language Processing (NLP), and Large Language Model (LLM) solutions. The role focuses on building intelligent systems for entity extraction, classification, semantic retrieval, ranking, and knowledge graph applications using large-scale enterprise datasets.

The ideal candidate will have hands-on experience with Python, transformer models, applied NLP, and machine learning, along with a strong understanding of model evaluation and production deployment. This is an opportunity to work on complex AI challenges involving millions of records while building scalable solutions that deliver measurable business value.

Requirements

Key Responsibilities

• Fine-tune transformer models and LLMs using techniques such asLoRA and QLoRAfor entity extraction, classification, and reranking.
• Develop NLP models to extract, classify, and link entities from resumes, job descriptions, and other unstructured enterprise data.
• Build prediction models, knowledge graphs, and graph traversal solutions to represent relationships between skills, roles, and competencies.
• Design and implement semantic retrieval, clustering, and learning-to-rank pipelines across large taxonomies and datasets.
• Develop embedding-based search solutions using bi-encoders, cross-encoders, and related retrieval techniques.
• Apply classical machine learning methods, including gradient boosting, logistic regression, clustering, and dimensionality reduction.
• Design evaluation frameworks, select appropriate metrics, analyse model errors, and validate performance through systematic experiments.
• Explore datasets containing millions of records using Python, SQL, and data-processing tools.
• Deploy, monitor, and improve machine learning models in production environments.
• Use experiment tracking, model versioning, Docker, and cloud infrastructure to support reproducible ML workflows.
• Collaborate with product, data, and engineering teams to translate business problems into effective AI solutions.
• Document experiments, communicate findings, and continuously improve model accuracy, efficiency, and reliability.

What Makes You a Great Fit

• 1+ years of relevant experiencein applied machine learning, NLP, or data science; experience expectations may vary between Data Scientist I and II.
• Strong proficiency inPython, with hands-on experience using PyTorch, Hugging Face Transformers, sentence-transformers, pandas, and scikit-learn.
• Practical experience fine-tuning transformer models on custom datasets for named entity recognition, classification, or related NLP tasks.
• Experience with LLM fine-tuning and parameter-efficient techniques such asPEFT, LoRA, or QLoRA.
• Strong understanding of embeddings, attention mechanisms, contrastive learning, semantic search, and information retrieval.
• Working knowledge of classical machine learning algorithms and statistical evaluation methods.
• Experience with SQL and the ability to analyse large-scale datasets.
• Understanding of model evaluation, sampling, statistical significance, calibration, and metric selection.
• Familiarity with ML deployment practices, experiment tracking, model versioning, Docker, and GPU-based training or serving on AWS.
• Experience with learning-to-rank, knowledge graphs, entity linking, or large-scale taxonomies is an advantage.
• Familiarity with Neo4j, Cypher, pgvector, graph ML, GLiNER, NuNER, vLLM, ONNX, AWS Bedrock, or SageMaker is a plus.
• Strong analytical thinking, problem-solving, and written communication skills.
• Ability to work collaboratively, take ownership of experiments, and translate research into production-ready solutions.
• A strong interest in applied AI, modern NLP, LLM systems, and continuous experimentation.