Data Scientist GenAI and Intelligent Automation

Tata Consultancy Services; Oakland (CA); On-site; USD 160,000 - 180,000; Full time

Benefits offered by this job

Discretionary Annual Incentive

Comprehensive Medical Coverage: Health

Dental & Vision

Disability Planning & Insurance

Commuter Benefits

Job summary

Tata Consultancy Services is seeking a Data Scientist to design, build, and operationalize ML, GenAI, and predictive models powering an enterprise-scale SP&D platform. The role focuses on cost estimation, calibration, compliance intelligence, and document/image interpretation, working closely with GenAI agents, cloud architects, and domain SMEs.

The candidate will develop regression and calibration models, work on data from SAP/EES sources, and contribute to MLOps practices, with a focus on

Qualifications

• Strong foundation in Data Science, Machine Learning, and Statistics.
• Hands-on experience with Python, scikit-learn, XGBoost, data analysis libraries (NumPy, Pandas).
• Experience building regression and calibration models.
• Strong understanding of model evaluation metrics.
• Experience working with large, complex enterprise datasets.
• Ability to explain model outputs in business-friendly language.

Responsibilities

• Machine Learning & Predictive Modeling: Design, develop, and tune ML models using XGBoost, Random Forest, scikit-learn and related frameworks. Evaluate model performance using MAE, RMSE, R², and error distribution analysis.
• GenAI & Agent Driven AI: Collaborate with AI Engineers to embed ML models into GenAI driven, multi-agent workflows. Work with RAG pipelines for document intelligence and contextual Q&A. Enable human-readable explanations for predictions and recommendations.
• Data Engineering & Feature Development: Analyze structured and unstructured datasets from historical estimates, actual costs, documents, and images. Perform feature engineering from SAP/EES data, historical project attributes, regulatory and standards documentation. Ensure data quality, normalization, and anomaly detection.
• Image & Non Text Analytics (Preferred): Support AI image analysis use cases: classification and attribute extraction from site photos and drawings, compliance signals against engineering standards, collaborate on pipelines using computer vision outputs and ML inference.
• MLOps & Model Lifecycle: Support model training, validation, and runtime invocation within cloud-native platforms. Work with DevOps and AI teams on model versioning, reproducibility, monitoring for drift, bias, and performance degradation. Provide inputs for MLOps/LLMOps pipelines and governance dashboards.

Skills

Data Science

Machine Learning

Statistics

Python

scikit-learn

XGBoost

NumPy/Pandas

Education

Bachelor of Computer Science

Tools

Python

scikit-learn

XGBoost

NumPy

Pandas

Job description

Job Description

Data Scientist to design, build, and operationalize ML, GenAI, and predictive models that power an enterprise scale AI driven Service Planning & Design (SP&D) platform. The role focuses on cost estimation, calibration, compliance intelligence, and document/image interpretation, working closely with GenAI agents, cloud architects, and domain SMEs.

Must Have Technical/Functional Skill

Roles & Responsibilities

• Machine Learning & Predictive Modeling: Design, develop, and tune ML models using XGBoost, Random Forest, scikit-learn and related frameworks. Evaluate model performance using MAE, RMSE, R², and error distribution analysis.
• GenAI & Agent Driven AI: Collaborate with AI Engineers to embed ML models into GenAI driven, multi-agent workflows. Work with RAG pipelines for document intelligence and contextual Q&A. Enable human-readable explanations for predictions and recommendations.
• Data Engineering & Feature Development: Analyze structured and unstructured datasets from historical estimates, actual costs, documents, and images. Perform feature engineering from SAP/EES data, historical project attributes, regulatory and standards documentation. Ensure data quality, normalization, and anomaly detection.
• Image & Non Text Analytics (Preferred): Support AI image analysis use cases: classification and attribute extraction from site photos and drawings, compliance signals against engineering standards, collaborate on pipelines using computer vision outputs and ML inference.
• MLOps & Model Lifecycle: Support model training, validation, and runtime invocation within cloud-native platforms. Work with DevOps and AI teams on model versioning, reproducibility, monitoring for drift, bias, and performance degradation. Provide inputs for MLOps/LLMOps pipelines and governance dashboards.

Required Skills & Experience

• Strong foundation in Data Science, Machine Learning, and Statistics.
• Hands‑on experience with Python, scikit-learn, XGBoost, data analysis libraries (NumPy, Pandas).
• Experience building regression and calibration models.
• Strong understanding of model evaluation metrics.
• Experience working with large, complex enterprise datasets.
• Ability to explain model outputs in business‑friendly language.

Preferred Skills

• Exposure to GenAI/LLM-enabled systems.
• Experience with RAG pipelines and vector search concepts.
• Familiarity with computer vision outputs and non‑text data analysis.
• Experience in utilities, infrastructure, or regulated industries.
• Understanding of AI governance, explainability, and auditability.

Salary Range

$160,000-$180,000 a year

Employee Benefits Summary

Discretionary Annual Incentive.

Comprehensive Medical Coverage: Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans.

Family Support: Maternal & Parental Leaves.

Insurance Options: Auto & Home Insurance, Identity Theft Protection.

Convenience & Professional Growth: Commuter Benefits & Certification & Training Reimbursement.

Time Off: Vacation, Time Off, Sick Leave & Holidays.

Legal & Financial Assistance: Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing.

Qualifications

Bachelor of Computer Science