Data Scientist

Role Overview:

This role supports one of our clients as we seek a talented and technically strong Data Scientist I / II to develop and deploy production-grade machine learning, Natural Language Processing (NLP), and Large Language Model (LLM) solutions. The position focuses on building intelligent systems for entity extraction, classification, semantic retrieval, ranking, and knowledge graph applications using large-scale enterprise datasets.

Key Responsibilities

• Fine-tune transformer models and LLMs using techniques such as LoRA and QLoRA for 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.

Ideal Candidate Profile

• 1+ years of relevant experience in applied machine learning, NLP, or data science; expectations may vary between Data Scientist I and II levels.
• Strong proficiency in Python 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 as PEFT, 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.
• Familiarity 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.