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# Data Scientist

- **Company:** Tata Consultancy Services Limited
- **Location:** Charlotte, NC, United States
- **Salary:** $120,000.0 - $130,000.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Data Analysis, JIRA, Microsoft Azure, Big Data, Cloud Computing, Content Analysis, Data Mining, Data Visualization, Relational Databases, Fraud Prevention and Detection, Apache Hadoop, Python (Programming Language), Machine Learning, Power BI, Tensorflow, Standard Sql, SQL Databases, Tableau (Software), Pytorch, Large Language Models, Prompt Engineering, Deep Learning, Pandas, Scikit Learn, Information Technology, Atlassian Tools, Machine Learning Operations, GPT
- **Published:** June 7, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=47956bbc84ca4407

## About the Role

Do you have experience in SQL?, Do you have a Master's degree?, Master's or PhD in Computer Science, Statistics, Mathematics, or a related field.
Proven experience as a Data Scientist in banking or a similar domain.
Proficiency in Python or R and data science libraries (pandas, scikit-learn, TensorFlow, PyTorch).
Hands-on LLM experience (OpenAI GPT, Llama, etc.), including prompt engineering and fine-tuning.
Strong understanding of statistics, machine learning, and data mining techniques.
Experience with visualization tools (Tableau, Power BI).
Experience with Big Data platforms (Hadoop).
Strong SQL and relational database skills.
Excellent problem-solving and communication skills.
Experience with cloud platforms (AWS, Azure, or GCP) is a plus.
Exposure to NLP, deep learning, or time series analysis.
Experience in deploying models to production.
Knowledge of banking regulatory and compliance requirements.
Familiarity with MLOps practices and Agile tools (JIRA or Rally)., Qualifications : BACHELOR OF COMPUTER SCIENCE

## Description

We are seeking a Data Scientist with experience in banking and finance to join our team. The ideal candidate will have a strong background in statistical analysis, machine learning, and large language models (LLMs), along with the ability to translate business problems into actionable data-driven solutions., Analyze large financial datasets to extract insights and support business decisions.
Develop, implement, and evaluate machine learning models for banking and finance use cases (e.g., risk modeling, fraud detection, customer segmentation).
Apply and fine-tune large language models (LLMs) for tasks such as document analysis, customer communication, and regulatory compliance.
Collaborate with cross-functional teams to understand business requirements and deliver solutions.
Communicate findings through reports, dashboards, and presentations.
Work with data engineers to ensure data quality and pipeline reliability.
Statistical analysis,Data analysis/insights and business problem framing


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