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.
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