Data Scientist
Tata Consultancy Services
•
Addison, TX
0
0
0
0
Not Applicable
Posted April 3, 2026
Job link
Thinking about this job
Responsibilities
Responsibilities
- Experience with NLP, deep learning, or time series analysis.
- Experience deploying models to production environments.
- Knowledge of regulatory requirements and compliance in banking and finance.
- Familiarity with MLOps practices and tools.
- Hands-on experience with large language models (e.g., OpenAI GPT, Llama, or similar), including fine-tuning and prompt engineering.
- Analyze large financial datasets to extract insights and support business decisions.
- Develop, implement, and evaluate machine learning models and algorithms tailored to 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 data-driven solutions.
- Communicate findings and recommendations through reports, dashboards, and presentations.
- Work with data engineers to ensure data quality and pipeline reliability.
Not Met Priorities
What still needs stronger evidence
Requirements
- Primary: Artificial intelligence, ML, LLM
- Secondary: Data science, Python, NLP, Agile tools
- Experience: 10+
- Experience with NLP, deep learning, or time series analysis.
- Experience deploying models to production environments.
- Knowledge of regulatory requirements and compliance in banking and finance.
- Familiarity with MLOps practices and tools.
- Experience with Agile methodology and tools (JIRA or Rally)
- Proven experience as a Data Scientist in Banking or a similar domain
- Proficiency in Python or R, and experience with data science libraries (e.g., pandas, scikit-learn, TensorFlow, PyTorch).
- Hands-on experience with large language models (e.g., OpenAI GPT, Llama, or similar), including fine-tuning and prompt engineering.
- Strong knowledge of statistics, machine learning, and data mining techniques.
- Experience with data visualization tools (e.g., Tableau, Power BI).
- Experience with Big Data Platforms (Hadoop).
- Familiarity with SQL and working with relational databases.
- Excellent problem-solving, communication, and collaboration skills.
- Analyze large financial datasets to extract insights and support business decisions.
- Develop, implement, and evaluate machine learning models and algorithms tailored to 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.
- Work with data engineers to ensure data quality and pipeline reliability.
Preferred Skills
- Secondary: Data science, Python, NLP, Agile tools
- Proven experience as a Data Scientist in Banking or a similar domain
- Proficiency in Python or R, and experience with data science libraries (e.g., pandas, scikit-learn, TensorFlow, PyTorch).
- Experience with cloud platforms (AWS, Azure, or GCP) is a plus.
Education
- (Not required) – Data scientist
- (Not required) – Primary: Artificial intelligence, ML, LLM
- (Not required) – Secondary: Data science, Python, NLP, Agile tools
- (Not required) – Qualifications: BACHELOR OF COMPUTER SCIENCE
Job Description
Data scientist
Must Have Technical/Functional Skills
Primary: Artificial intelligence, ML, LLM
Secondary: Data science, Python, NLP, Agile tools
Experience: 10+
Roles & Responsibilities
Experience with NLP, deep learning, or time series analysis.
Experience deploying models to production environments.
Knowledge of regulatory requirements and compliance in banking and finance.
Familiarity with MLOps practices and tools.
Experience with Agile methodology and tools (JIRA or Rally)
Proven experience as a Data Scientist in Banking or a similar domain
Proficiency in Python or R, and experience with data science libraries (e.g., pandas, scikit-learn, TensorFlow, PyTorch).
Hands-on experience with large language models (e.g., OpenAI GPT, Llama, or similar), including fine-tuning and prompt engineering.
Strong knowledge of statistics, machine learning, and data mining techniques.
Experience with data visualization tools (e.g., Tableau, Power BI).
Experience with Big Data Platforms (Hadoop).
Familiarity with SQL and working with relational databases.
Excellent problem-solving, communication, and collaboration skills.
Experience with cloud platforms (AWS, Azure, or GCP) is a plus.
Analyze large financial datasets to extract insights and support business decisions.
Develop, implement, and evaluate machine learning models and algorithms tailored to 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 data-driven solutions.
Communicate findings and recommendations through reports, dashboards, and presentations.
Work with data engineers to ensure data quality and pipeline reliability.
Salary Range- $120,000-$135,000 a year
Qualifications: BACHELOR OF COMPUTER SCIENCE
Data scientist
Must Have Technical/Functional Skills
Primary: Artificial intelligence, ML, LLM
Secondary: Data science, Python, NLP, Agile tools
Experience: 10+
Roles & Responsibilities
Experience with NLP, deep learning, or time series analysis.
Experience deploying models to production environments.
Knowledge of regulatory requirements and compliance in banking and finance.
Familiarity with MLOps practices and tools.
Experience with Agile methodology and tools (JIRA or Rally)
Proven experience as a Data Scientist in Banking or a similar domain
Proficiency in Python or R, and experience with data science libraries (e.g., pandas, scikit-learn, TensorFlow, PyTorch).
Hands-on experience with large language models (e.g., OpenAI GPT, Llama, or similar), including fine-tuning and prompt engineering.
Strong knowledge of statistics, machine learning, and data mining techniques.
Experience with data visualization tools (e.g., Tableau, Power BI).
Experience with Big Data Platforms (Hadoop).
Familiarity with SQL and working with relational databases.
Excellent problem-solving, communication, and collaboration skills.
Experience with cloud platforms (AWS, Azure, or GCP) is a plus.
Analyze large financial datasets to extract insights and support business decisions.
Develop, implement, and evaluate machine learning models and algorithms tailored to 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 data-driven solutions.
Communicate findings and recommendations through reports, dashboards, and presentations.
Work with data engineers to ensure data quality and pipeline reliability.
Salary Range- $120,000-$135,000 a year
Qualifications: BACHELOR OF COMPUTER SCIENCE