TCS is Having walkin Drive for skill Data Scientist At TCS Banglore Location on 12th June 2026 Saturday

exp req:6+ yrs

Interview Venue: TCS Brigade Bhuwalka Icon, No.1, ITPL Main Road, Pattandur Agrahara, Whitefield, Bengaluru, Karnataka 560066

JD

Must Have – Data Science

• Strong expertise in Python (NumPy, Pandas, Scikit-learn)
• Hands-on experience with ML algorithms (Regression, Classification, Clustering)
• Strong knowledge of Statistics & Probability
• Experience in data preprocessing, feature engineering, and data analysis
• Proven experience building end-to-end ML models (training, validation, deployment)
• Strong proficiency in SQL and working with large datasets
• Experience with model evaluation & tuning techniques
• Exposure to Deep Learning (TensorFlow / PyTorch) for mid–senior roles
• Experience in at least one domain:
• NLP / Time Series / Recommendation Systems
• Basic understanding of ML model deployment (APIs / batch / real-time)
• Familiarity with Cloud platforms (AWS / Azure / GCP)

#jobvacancy #jobopening #jobs #bangalore DataFrontier Innovations pvt ltd

Data Scientist (Early Career) – Healthcare AI & Agentic Systems

Location: Bangalore (Hybrid)

Experience: 2–4 years

Company: DataFrontier Innovations Pvt. Ltd.

Email:

About DataFrontier

DataFrontier builds advanced AI, data engineering, and analytics systems that solve real-world, high-impact problems — especially in healthcare and enterprise environments.

We are currently working on:

• Falls prediction systems for elderly care using clinical and behavioral data

• Agentic AI systems that automate workflows and decision-making

• Production-grade data platforms handling sensitive, regulated datasets

If you want to build systems that actually get used — not just models that sit in notebooks — this role is for you.

Role Overview

We are looking for a hands-on Data Scientist who can work across the full lifecycle — from data understanding to model deployment — and is excited about applying AI in healthcare and automation.

This is not a pure research role. You will be building, shipping, and improving real systems.

What You’ll Work On

• Build and improve predictive models for falls risk using clinical, behavioral, and time-series data

• Design features from messy real-world healthcare datasets (EHR, sensor data, logs)

• Work on Agentic AI pipelines (LLMs + tools + workflows) to automate decision systems

• Develop and test models like XGBoost, Random Forest, time-series models, and hybrid approaches

• Collaborate with data engineers to build robust pipelines and feature stores

• Implement model explainability (SHAP, feature importance) for clinical usability

• Evaluate data quality and completeness for new customers (critical for healthcare deployments)

• Work closely with product and clients to translate business problems into ML solutions

Required Skills

• Strong fundamentals in Python (pandas, numpy, scikit-learn)

• Experience with ML models (classification, regression, tree-based models)

• Solid understanding of feature engineering and data preprocessing

• Familiarity with SQL and working with structured datasets

• Basic understanding of model evaluation metrics and validation techniques

• Exposure to real-world datasets (not just Kaggle-level clean data)

Good to Have (High Impact)

• Experience with healthcare data / EHR / clinical datasets

• Exposure to LLMs / LangChain / agentic frameworks

• Knowledge of time-series modeling

• Experience with cloud platforms (AWS / GCP)

• Understanding of data pipelines / ETL workflows

• Familiarity with model deployment or APIs

What We’re Looking For

• Someone who can think, not just code

• Comfortable working with unclean, incomplete, real-world data

• Willing to own problems end-to-end, not just tasks

• Curious about AI beyond standard ML — especially agentic systems

• Strong communication skills — ability to explain models to non-technical stakeholders

What This Role Is NOT

• Not a pure research role

• Not a “train model and forget” role

• Not limited to notebooks — you will work on production systems

Why Join Us

• Work on real healthcare impact problems

• Exposure to international clients and deployments

• Build next-gen systems in Agentic AI + predictive analytics

• Small team → high ownership and fast growth

How to Apply

Send your resume + 2–3 projects (GitHub / case studies) to:

Subject: Data Scientist – Early Career Application

If you’re looking for comfort, this role is not for you.

If you’re looking to build something meaningful, let’s talk.

• Design and implement end-to-end evaluation frameworks to assess performance, reliability, and safety of multi-agent AI systems
• Lead experimentation and A/B testing efforts to systematically test hypotheses, validate model improvements, and track performance across agent iterations
• Curate and maintain high-quality ground truth datasets to enable accurate, reproducible evaluation of multi-agent outputs
• Identify and address reliability and accuracy gaps across agent workflows, failure modes, and edge cases in production-like environments
• Stay current on emerging research in agentic AI, LLM evaluation, and multi-agent coordination to continuously improve framework design

Technical Skills

• Proficiency in Python and ML frameworks
• Hands-on experience with LLM APIs and agentic frameworks (LangChain, LlamaIndex, Semetic KernalI)
• Familiarity with evaluation tooling (Ragas, DeepEval, LangSmith, or similar)
• Experience with data pipelines, experiment tracking (MLflow, W&B), and CI/CD for ML workflows
• Strong foundation in statistics, NLP, prompt engineering, experimental design, and A/B testing methodology
• Proficiency in Azure ML, Azure OpenAI Service, and Azure AI Foundry for model deployment, evaluation, and orchestration
• Familiarity with Azure Monitor and Application Insights for tracking reliability and performance of deployed agent systems

• Design and implement end-to-end evaluation frameworks to assess performance, reliability, and safety of multi-agent AI systems
• Lead experimentation and A/B testing efforts to systematically test hypotheses, validate model improvements, and track performance across agent iterations
• Curate and maintain high-quality ground truth datasets to enable accurate, reproducible evaluation of multi-agent outputs
• Identify and address reliability and accuracy gaps across agent workflows, failure modes, and edge cases in production-like environments
• Stay current on emerging research in agentic AI, LLM evaluation, and multi-agent coordination to continuously improve framework design

Technical Skills

• Proficiency in Python and ML frameworks
• Hands-on experience with LLM APIs and agentic frameworks (LangChain, LlamaIndex, Semetic KernalI)
• Familiarity with evaluation tooling (Ragas, DeepEval, LangSmith, or similar)
• Experience with data pipelines, experiment tracking (MLflow, W&B), and CI/CD for ML workflows
• Strong foundation in statistics, NLP, prompt engineering, experimental design, and A/B testing methodology
• Proficiency in Azure ML, Azure OpenAI Service, and Azure AI Foundry for model deployment, evaluation, and orchestration
• Familiarity with Azure Monitor and Application Insights for tracking reliability and performance of deployed agent systems

• Design and implement end-to-end evaluation frameworks to assess performance, reliability, and safety of multi-agent AI systems
• Lead experimentation and A/B testing efforts to systematically test hypotheses, validate model improvements, and track performance across agent iterations
• Curate and maintain high-quality ground truth datasets to enable accurate, reproducible evaluation of multi-agent outputs
• Identify and address reliability and accuracy gaps across agent workflows, failure modes, and edge cases in production-like environments
• Stay current on emerging research in agentic AI, LLM evaluation, and multi-agent coordination to continuously improve framework design

Technical Skills

• Proficiency in Python and ML frameworks
• Hands-on experience with LLM APIs and agentic frameworks (LangChain, LlamaIndex, Semetic KernalI)
• Familiarity with evaluation tooling (Ragas, DeepEval, LangSmith, or similar)
• Experience with data pipelines, experiment tracking (MLflow, W&B), and CI/CD for ML workflows
• Strong foundation in statistics, NLP, prompt engineering, experimental design, and A/B testing methodology
• Proficiency in Azure ML, Azure OpenAI Service, and Azure AI Foundry for model deployment, evaluation, and orchestration
• Familiarity with Azure Monitor and Application Insights for tracking reliability and performance of deployed agent systems

• Design and implement end-to-end evaluation frameworks to assess performance, reliability, and safety of multi-agent AI systems
• Lead experimentation and A/B testing efforts to systematically test hypotheses, validate model improvements, and track performance across agent iterations
• Curate and maintain high-quality ground truth datasets to enable accurate, reproducible evaluation of multi-agent outputs
• Identify and address reliability and accuracy gaps across agent workflows, failure modes, and edge cases in production-like environments
• Stay current on emerging research in agentic AI, LLM evaluation, and multi-agent coordination to continuously improve framework design

Technical Skills

• Proficiency in Python and ML frameworks
• Hands-on experience with LLM APIs and agentic frameworks (LangChain, LlamaIndex, Semetic KernalI)
• Familiarity with evaluation tooling (Ragas, DeepEval, LangSmith, or similar)
• Experience with data pipelines, experiment tracking (MLflow, W&B), and CI/CD for ML workflows
• Strong foundation in statistics, NLP, prompt engineering, experimental design, and A/B testing methodology
• Proficiency in Azure ML, Azure OpenAI Service, and Azure AI Foundry for model deployment, evaluation, and orchestration
• Familiarity with Azure Monitor and Application Insights for tracking reliability and performance of deployed agent systems

• Design and implement end-to-end evaluation frameworks to assess performance, reliability, and safety of multi-agent AI systems
• Lead experimentation and A/B testing efforts to systematically test hypotheses, validate model improvements, and track performance across agent iterations
• Curate and maintain high-quality ground truth datasets to enable accurate, reproducible evaluation of multi-agent outputs
• Identify and address reliability and accuracy gaps across agent workflows, failure modes, and edge cases in production-like environments
• Stay current on emerging research in agentic AI, LLM evaluation, and multi-agent coordination to continuously improve framework design

Technical Skills

• Proficiency in Python and ML frameworks
• Hands-on experience with LLM APIs and agentic frameworks (LangChain, LlamaIndex, Semetic KernalI)
• Familiarity with evaluation tooling (Ragas, DeepEval, LangSmith, or similar)
• Experience with data pipelines, experiment tracking (MLflow, W&B), and CI/CD for ML workflows
• Strong foundation in statistics, NLP, prompt engineering, experimental design, and A/B testing methodology
• Proficiency in Azure ML, Azure OpenAI Service, and Azure AI Foundry for model deployment, evaluation, and orchestration
• Familiarity with Azure Monitor and Application Insights for tracking reliability and performance of deployed agent systems

• Design and implement end-to-end evaluation frameworks to assess performance, reliability, and safety of multi-agent AI systems
• Lead experimentation and A/B testing efforts to systematically test hypotheses, validate model improvements, and track performance across agent iterations
• Curate and maintain high-quality ground truth datasets to enable accurate, reproducible evaluation of multi-agent outputs
• Identify and address reliability and accuracy gaps across agent workflows, failure modes, and edge cases in production-like environments
• Stay current on emerging research in agentic AI, LLM evaluation, and multi-agent coordination to continuously improve framework design

Technical Skills

• Proficiency in Python and ML frameworks
• Hands-on experience with LLM APIs and agentic frameworks (LangChain, LlamaIndex, Semetic KernalI)
• Familiarity with evaluation tooling (Ragas, DeepEval, LangSmith, or similar)
• Experience with data pipelines, experiment tracking (MLflow, W&B), and CI/CD for ML workflows
• Strong foundation in statistics, NLP, prompt engineering, experimental design, and A/B testing methodology
• Proficiency in Azure ML, Azure OpenAI Service, and Azure AI Foundry for model deployment, evaluation, and orchestration
• Familiarity with Azure Monitor and Application Insights for tracking reliability and performance of deployed agent systems

• Design and implement end-to-end evaluation frameworks to assess performance, reliability, and safety of multi-agent AI systems
• Lead experimentation and A/B testing efforts to systematically test hypotheses, validate model improvements, and track performance across agent iterations
• Curate and maintain high-quality ground truth datasets to enable accurate, reproducible evaluation of multi-agent outputs
• Identify and address reliability and accuracy gaps across agent workflows, failure modes, and edge cases in production-like environments
• Stay current on emerging research in agentic AI, LLM evaluation, and multi-agent coordination to continuously improve framework design

Technical Skills

• Proficiency in Python and ML frameworks
• Hands-on experience with LLM APIs and agentic frameworks (LangChain, LlamaIndex, Semetic KernalI)
• Familiarity with evaluation tooling (Ragas, DeepEval, LangSmith, or similar)
• Experience with data pipelines, experiment tracking (MLflow, W&B), and CI/CD for ML workflows
• Strong foundation in statistics, NLP, prompt engineering, experimental design, and A/B testing methodology
• Proficiency in Azure ML, Azure OpenAI Service, and Azure AI Foundry for model deployment, evaluation, and orchestration
• Familiarity with Azure Monitor and Application Insights for tracking reliability and performance of deployed agent systems