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Data Scientist

Reads like a ML Engineer role: putting models into production.

Bengaluru, Karnataka, India

Pay
Pay not listed
Work setting
On-site — work setting source
Work setup in the employer’s posting: On-site
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Employment
Full-time — employment source
Employment type in the employer’s posting: Full-time
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What you’ll work on

Full posting
  • 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.

From the employer’s posting
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.
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.
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.

Tools in this posting

  • Python
  • SQL
  • AWS
  • Docker
  • Hugging Face
  • pandas
  • PyTorch
  • scikit-learn
  • Transformers
Source — Tool mentions in context
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
- 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.
- 1–3 years of experience for Data Scientist I or 3–5 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.
- 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.
- 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.
- 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.

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Actively hiringFirst seen by us 10 days ago, still listed, and not a repost of an earlier posting.

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First seen by us
Oct 21, 2026 · 10 days ago
Recorded sightings
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Last seen by us
Oct 31, 2026

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Job description

View original posting ↗

𝗧𝗵𝗶𝘀 𝗿𝗼𝗹𝗲 𝗶𝘀 𝗳𝗼𝗿 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝗲𝗸𝗱𝗮𝘆'𝘀 𝗰𝗹𝗶𝗲𝗻𝘁𝘀

𝗦𝗮𝗹𝗮𝗿𝘆 𝗿𝗮𝗻𝗴𝗲: 𝗥𝘀 𝟮𝟬𝟬𝟬𝟬𝟬𝟬 - 𝗥𝘀 𝟳𝟬𝟬𝟬𝟬𝟬𝟬 (𝗶𝗲 𝗜𝗡𝗥 𝟮𝟬-𝟳𝟬 𝗟𝗣𝗔)

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

  • 1–3 years of experience for Data Scientist I or 3–5 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.

Source details

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Location & working pattern

Bengaluru, Karnataka, India

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