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Data Scientist

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Onsite

Data Scientist

Weekday AI

Data Scientist

Skills

SQL QueriesDockerPythonpandasPyTorchMachine LearningAIAWSNLP / Natural Language Processing

2 days ago

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Important information


Contract type:

Permanent

Location:

Bengaluru, Karnataka, India

Work mode:

Onsite

Published on:

22 October 2026

What they need


๐—ง๐—ต๐—ถ๐˜€ ๐—ฟ๐—ผ๐—น๐—ฒ ๐—ถ๐˜€ ๐—ณ๐—ผ๐—ฟ ๐—ผ๐—ป๐—ฒ ๐—ผ๐—ณ ๐˜๐—ต๐—ฒ ๐—ช๐—ฒ๐—ฒ๐—ธ๐—ฑ๐—ฎ๐˜†'๐˜€ ๐—ฐ๐—น๐—ถ๐—ฒ๐—ป๐˜๐˜€

๐—ฆ๐—ฎ๐—น๐—ฎ๐—ฟ๐˜† ๐—ฟ๐—ฎ๐—ป๐—ด๐—ฒ: ๐—ฅ๐˜€ ๐Ÿฎ๐Ÿฌ๐Ÿฌ๐Ÿฌ๐Ÿฌ๐Ÿฌ๐Ÿฌ - ๐—ฅ๐˜€ ๐Ÿณ๐Ÿฌ๐Ÿฌ๐Ÿฌ๐Ÿฌ๐Ÿฌ๐Ÿฌ (๐—ถ๐—ฒ ๐—œ๐—ก๐—ฅ ๐Ÿฎ๐Ÿฌ-๐Ÿณ๐Ÿฌ ๐—Ÿ๐—ฃ๐—”)

Experience: 1+ yrs

Location: Bangalore

We are looking for talented Data Scientists (Level I / II) to build and deploy production-grade machine learning, natural language processing (NLP), and large language model (LLM) solutions for enterprise talent and workforce intelligence platforms.

The ideal candidates will have strong Python programming skills, hands-on experience with applied NLP or machine learning, and practical knowledge of transformer models, information retrieval, and model evaluation. This role offers the opportunity to work on large-scale AI systems that extract insights from resumes, job descriptions, skills taxonomies, and other enterprise datasets.

Requirements

Key Responsibilities

  • Fine-tune transformer models and LLMs using techniques such as LoRA and QLoRA for entity extraction, classification, and reranking.
  • Develop NLP pipelines to extract structured entities, skills, and relevant information from resumes, job descriptions, and other unstructured datasets.
  • Build predictive models for entity classification, entity linking, and skills intelligence.
  • Design and implement knowledge graphs, graph traversal methods, and relationships across large skills taxonomies.
  • Develop retrieval, clustering, semantic search, and learning-to-rank pipelines using embeddings, bi-encoders, and cross-encoders.
  • Apply classical machine learning techniques, including gradient boosting, logistic regression, clustering, and dimensionality reduction.
  • Design robust evaluation frameworks, analyse model errors, select appropriate metrics, and validate improvements through statistical analysis.
  • Process and analyse large datasets containing millions of records using Python, SQL, and relevant data processing tools.
  • Deploy and optimise machine learning models for production environments, ensuring scalability, reliability, and performance.
  • Use experiment tracking, model versioning, Docker, and AWS-based GPU training and serving workflows.
  • Collaborate with engineering and product teams to integrate AI capabilities into enterprise applications.
  • Document experiments, communicate findings, and continuously improve model quality, efficiency, and business impact.

What Makes You a Great Fit

  • 1โ€“3 years of experience for Data Scientist I or 3โ€“5 years for Data Scientist II in applied NLP, machine learning, or a related field.
  • Strong proficiency in Python and practical experience with PyTorch, Hugging Face Transformers, sentence-transformers, pandas, and scikit-learn.
  • Hands-on experience fine-tuning transformer models on custom datasets for named entity recognition (NER), classification, or LLM-based tasks using PEFT techniques such as LoRA/QLoRA.
  • Strong understanding of embeddings, attention mechanisms, contrastive learning, semantic retrieval, bi-encoders, and cross-encoders.
  • Working knowledge of classical machine learning algorithms and statistical evaluation methods.
  • Experience with SQL and analysing large-scale datasets.
  • Understanding of learning-to-rank systems, knowledge graphs, entity linking, or large taxonomies.
  • Familiarity with ML lifecycle practices, including experiment tracking, model versioning, Docker, and cloud-based model deployment.
  • Strong analytical thinking, problem-solving ability, and attention to model quality.
  • Clear written communication skills and the ability to explain experiments, results, and technical trade-offs.

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