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Data Scientist

Weekday AI / Technology, Information and Internet
Bengaluru, Karnataka, IndiaINR2,000,000 - INR7,000,000 / YEAROn-site
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AI Summary

The role involves developing and deploying production-grade machine learning and NLP solutions, including fine-tuning LLMs and building predictive models. You will collaborate with engineering teams to integrate AI capabilities into enterprise platforms while ensuring model scalability and performance.

Candidates must have 1-5 years of experience in applied NLP or machine learning with strong proficiency in Python and deep learning frameworks. Practical knowledge of transformer models, SQL, and cloud-based deployment workflows is essential for this position.

Job Details
Job Type: FULL TIME
Visa Sponsorship: No
Salary: INR2,000,000 - INR7,000,000 / YEAR
Work Arrangement: On-site
Experience Level: 2 - 5 years
Hours: 40 hrs/week
Language: English
Key Skills
PythonMachine LearningNLPLLMPyTorchHugging Face TransformersSQLDockerAWSTransformer ModelsEntity ExtractionKnowledge GraphsSemantic SearchLearning-to-rankStatistical AnalysisData Processing
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Experience: 1+ yrs

Location: Bangalore

We are looking for talentedย Data Scientists (Level I / II)ย 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.

Key Responsibilities
  • Fine-tune transformer models and LLMs using techniques such asย LoRA and QLoRAย 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 IIย 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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Technology, Information and Internet
60 employees
Most staff in IND