Data Scientist/ NLP

Data Scientist NLP Job Overview We are looking for a hands-on Data Scientist specializing in Natural Language Processing (NLP) to design, develop, evaluate, and deploy production-grade NLP and machine learning solutions for complex, text-driven workflows.

The ideal candidate should have strong expertise in Python, SQL, NLP, Transformers, embeddings, semantic search, information retrieval, and machine learning , with the ability to take solutions from experimentation through production deployment.

Key Responsibilities

• Design and develop NLP and machine learning pipelines to process noisy, heterogeneous text data and transform it into clean semantic representations for modeling, retrieval, analytics, and downstream applications.

• Build and optimize semantic search and retrieval systems using embeddings, vector databases, similarity search, and ranking techniques.

• Develop solutions for candidate ranking, out-of-vocabulary handling, semantic matching, and information discovery.

• Design, implement, and evaluate supervised and hybrid ML approaches, including:

• Multi-output classification

• Hierarchical classification

• Named Entity Recognition (NER)

• Entity extraction and parsing

• Clustering

• Rule-based + ML hybrid systems

• Work with transformer-based models for text understanding, classification, similarity, extraction, and retrieval use cases.

• Perform detailed analysis of relationships and decision boundaries across free-text fields using:

• Conditional distributions

• Entropy

• Mutual information

• Directional association

• Embeddings

• Predictive ablation studies

• Design experiments and establish appropriate model evaluation metrics and benchmarks.

• Compare different models and approaches based on accuracy, performance, scalability, latency, and business impact.

• Fine-tune and evaluate transformer models using frameworks such as Hugging Face and SentenceTransformers.

• Deploy, monitor, troubleshoot, and continuously improve production ML/NLP services.

• Collaborate closely with platform, backend, data engineering, and product teams to integrate ML solutions into production systems.

• Communicate technical findings, model performance, and trade-offs effectively to both technical and non-technical stakeholders.

Required Skills

• Strong programming experience in Python and SQL.

• Hands-on experience developing production-grade data pipelines and machine learning workflows.

• Strong understanding of Natural Language Processing (NLP) and text analytics.

• Practical experience with one or more of the following:

• Text Classification

• Semantic Similarity

• Text Embeddings

• Information Retrieval

• Search & Ranking

• Clustering

• Named Entity Recognition (NER)

• Entity Extraction

• Hands-on experience with:

• Hugging Face

• SentenceTransformers

• Tokenization

• Transformer-based models

• Model fine-tuning

• Model evaluation

• Strong understanding of embeddings, vector search, and similarity search.

• Knowledge of cosine similarity and Approximate Nearest Neighbor (ANN) search methods.

• Familiarity with information retrieval metrics such as:

• Recall@K

• MRR (Mean Reciprocal Rank)

• NDCG (Normalized Discounted Cumulative Gain)

• Strong analytical and problem-solving skills with the ability to design experiments, define evaluation metrics, and interpret model results.

• Ability to evaluate model trade-offs and clearly communicate technical findings.

Nice to Have

• Experience in healthcare, medical imaging, document intelligence, enterprise search, recommendation systems, knowledge retrieval, or routing systems.

• Knowledge of healthcare and enterprise data standards such as:

• DICOM

• PACS/RIS

• HL7

• FHIR

• Experience with MLOps and production ML systems.

• Experience with cloud platforms and API-based model deployment.

• Experience with model serving, monitoring, logging, and performance optimization.

• Knowledge of Responsible AI, data privacy, and secure handling of sensitive text data.

• Experience working with vector databases/search platforms and large-scale retrieval systems.

Preferred Candidate Profile The ideal candidate will have a combination of NLP expertise, machine learning fundamentals, information retrieval knowledge, and production engineering experience . Candidates with experience building semantic search, embedding-based retrieval, classification, RAG, document intelligence, or enterprise search solutions will be highly preferred.

Core Skills

Python | SQL | NLP | Machine Learning | Hugging Face | SentenceTransformers | Transformers | Embeddings | Semantic Search | Vector Search | Information Retrieval | Ranking | Text Classification | NER | Clustering | Model Fine-tuning | Recall@K | MRR | NDCG