Senior Data Scientist

Tata Consultancy Services; Bengaluru; On-site; INR 2,500,000 - 4,200,000; Full time

Job summary

Tata Consultancy Services in Bengaluru is hiring a Senior Data Scientist to lead end-to-end ML/DL solution development for complex business challenges.

The role requires 5+ years of hands-on experience in Python, ML/DL, NLP, and MLOps on Azure/AWS, with strong data engineering and cloud deployment skills. Direct Walk-In Opportunity applies for candidates meeting the criteria.

Qualifications

• 15 years of full time education BE/B.TECH/BCA/MCA/MSC.
• Proven experience in building and deploying ML/DL models for large-scale data.

Responsibilities

• Lead end-to-end design, development, and deployment of ML/DL solutions.
• Define modeling strategies and select ML/DL techniques based on business impact.
• Oversee data science lifecycle governance from data acquisition to model monitoring.
• Drive production-grade deployment via MLOps on Azure and AWS.
• Mentor data science teams and establish best practices in Python engineering.

Skills

Python

Machine Learning

Deep Learning

NLP

LLMs

Azure

AWS

MLOps

Data science

Education

BE/B.Tech/BCA/MCA/MSC

Tools

Azure

AWS

Job description

Greeting from TCS!!!

We are hiring Senior Data Scientist role for Bangalore Location.

***Direct Walk-In Opportunity 5+ Years experience required***

Location: Tata Consultancy Services Kochi, TCS, Discoverer Building, INTERNATIONAL TECH PARK (ITPL), 4th, G1, Whitefield Main Rd, Pattandur Agrahara, Whitefield, Bengaluru, Karnataka 560066.

Date: 12th Sep 2026

Registration time: 9:00 AM - 12:30 PM

Role: Senior Data Scientist

Skills: Python, Machine Learning, Deep Learning, Data science, LLMS, Azure/AWS.

Experience: 5+ years

Location: Bangalore

Minimum Qualification

15 years of full time education BE/B.TECH/BCA/MCA/MSC

Job Description

Must Have Skills

• Designed, developed, and deployed Machine Learning and Deep Learning models for industrial challenges involving large-scale numerical, categorical, and time series data, as well as images and videos.
• Extensive experience in Python programming for statistical analysis, custom algorithm development, and business insight generation using ML/DL models.
• Proficient in optimization techniques including Genetic Algorithms, Linear and Quadratic Programming, and others
• Skilled in comprehensive data workflows: collection, exploratory analytics, data cleansing, feature selection, and model validation.
• Experience in deploying AI/ML solutions using MLOPs on cloud platforms such as Azure and AWS.
• Solid knowledge of NLP, Large Language Models, Retrieval-Augmented Generation, and ML algorithms such as Decision Trees, Clustering, Support Vector Machines, Artificial Neural Networks, LSTM, CNN, and YOLO, with awareness of their practical advantages and limitations.
• Motivated by continuous learning and mastery of emerging technologies and methodologies in the fields of artificial intelligence and machine learning.

Responsibility of / Expectations from the Role

• Lead end to end design, development, and deployment of ML/DL solutions for complex business and industrial problems involving structured, time series, and unstructured data.
• Define modeling strategies and technical approaches, selecting appropriate ML, DL, Computer Vision, Optimization, and LLM techniques based on problem constraints and business impact.
• Oversee data science lifecycle governance, including data acquisition, exploratory analysis, feature engineering, model validation, and performance monitoring.
• Drive production grade deployment through MLOps, ensuring scalable, secure, and reliable model operations on cloud platforms such as Azure and AWS.
• Lead development of advanced analytics and optimization solutions, translating business objectives into prescriptive and decision support models.
• Provide technical leadership and mentoring to data science teams, establishing best practices in Python engineering, experimentation, and model reliability.
• Collaborate with stakeholders to translate business needs into AI solutions, clearly communicating assumptions, risks, outcomes, and measurable value.
• Continuously evaluate emerging AI/ML and LLM technologies, driving innovation, POCs, and capability building aligned with organizational strategy.