Primary Skills: Data Science, Python, Machine Learning, NLP
Secondary Skills: Generative AI, OCR, Hugging Face, LangChain, Agile
We are seeking an experienced Senior Data Scientist with strong expertise in Python, Machine Learning, Natural Language Processing (NLP), and Generative AI to design, develop, and deploy intelligent document processing solutions.
The role involves building enterprise-scale ML/NLP solutions to extract meaningful insights from large volumes of structured and unstructured data. The ideal candidate should have hands-on experience in document classification, entity extraction, summarization, OCR technologies, and exposure to modern open-source GenAI models and frameworks.
The candidate will collaborate with business, technology, risk, and cross-functional teams to deliver scalable, intelligent automation solutions within an Agile development environment.
Design, develop, train, and deploy Machine Learning and NLP models for document processing and correspondence analysis.
Build and maintain end-to-end ML pipelines covering data ingestion, cleansing, transformation, feature engineering, model selection, and evaluation.
Develop supervised and unsupervised machine learning models for enterprise use cases.
Implement NLP solutions for Named Entity Recognition (NER), document classification, summarization, topic modeling, sentiment analysis, and dialogue systems.
Develop Generative AI solutions for document understanding, information extraction, summarization, and intelligent automation.
Work with open-source GenAI models and frameworks such as Hugging Face, LLaMA, Mistral, and LangChain.
Implement and optimize OCR-based document processing solutions for scanned and unstructured documents.
Process and analyze large-scale structured and unstructured datasets.
Perform model evaluation, performance tuning, validation, and optimization using appropriate evaluation metrics.
Develop scalable and production-ready ML/NLP applications.
Collaborate with business stakeholders, technology teams, and Risk/LOB teams to understand requirements and deliver business-focused solutions.
Follow software engineering best practices, including testing, CI/CD, deployment, and production support.
Participate in Agile ceremonies, sprint planning, development, and release activities.
1. Data Science & Machine Learning
Strong experience in Data Science and statistical analysis.
Hands-on experience with supervised and unsupervised machine learning algorithms.
Expertise in feature engineering, model training, validation, and optimization.
Experience with ensemble learning techniques and model evaluation metrics.
2. Programming
5+ years of programming experience.
Strong proficiency in Python.
Experience with Python-based ML and NLP libraries and frameworks.
3. Natural Language Processing (NLP)
Hands-on experience developing enterprise NLP solutions.
Experience in one or more of the following:
Named Entity Recognition (NER)
Document Classification
Document Summarization
Topic Modeling
Sentiment Analysis
Dialogue Systems
4. Generative AI & Document Intelligence
Exposure to open-source Generative AI models.
Experience with Hugging Face, LLaMA, Mistral, and LangChain.
Understanding of LLM-based document processing and intelligent automation.
Experience with OCR technologies and unstructured document processing.
5. ML Pipeline & Deployment
Experience building end-to-end ML/NLP pipelines.
Experience handling large-scale structured and unstructured datasets.
Knowledge of CI/CD, testing, production deployment, and model performance monitoring.
6. Agile Methodology
Experience working in Agile software development environments.
Familiarity with sprint planning, development cycles, and release management.
7-10 years of experience in Data Science, Machine Learning, or a related role.
Bachelor's degree in Computer Science, Data Science, or a related technical discipline.
Minimum 5 years of programming experience with strong Python proficiency.
At least 2 years of experience developing enterprise-scale ML/NLP solutions.
Proven experience building and deploying machine learning models and NLP pipelines.
Strong analytical, problem-solving, and communication skills.
Master's degree in Computer Science, Data Science, or a related field.
Experience working with enterprise-scale data ecosystems.
Ability to independently own and deliver technical initiatives.
Experience collaborating with multiple business and technology stakeholders.
Strong communication and presentation skills.
Ability to manage multiple priorities in a fast-paced environment.
Experience delivering solutions in banking, financial services, or other regulated enterprise environments is an added advantage.
