Senior Data Scientist: Vertex AI & BigQuery Architect

Cognizant; Atlanta (GA); Hybrid; USD 150,000 - 210,000; Full time

Job summary

Cognizant is seeking a senior data scientist to design and deploy robust analytics and ML solutions in a hybrid work model. You will leverage Python, Vertex AI, BigQuery, and GIT to address complex retail and merchandising challenges across the enterprise.

Responsibilities include developing scalable models, building data pipelines, and deploying production workflows while ensuring data governance and stakeholder alignment. Strong collaboration with product teams and analysts is essential.

Qualifications

• 10–12 years in advanced analytics and data science with production ML experience.
• Expert Python for data wrangling, statistics, and automation.
• Deep BigQuery experience: optimized queries and large-scale workloads.
• Experience with Vertex AI for experiment tracking and deployment.
• Strong source control using GIT in collaborative teams.

Responsibilities

• Develop scalable ML models to forecast, recommend and optimize business outcomes.
• Design end-to-end data pipelines for large datasets in BigQuery.
• Orchestrate production ML workflows on Vertex AI for reproducible experiments.
• Apply EDA to uncover patterns and opportunities across retail domains.
• Maintain versioned artifacts and experiments in a centralized repository.
• Collaborate with product, analytics, and engineering in hybrid teams.
• Develop interpretable features and ensure model fairness across segments.
• Evaluate approaches with robust metrics and backtesting frameworks.
• Communicate complex model behavior to nontechnical partners.

Skills

Python

BigQuery

Vertex AI

GIT

Data pipelines

Model development

Forecasting

Malware analytics

Tools

BigQuery

Vertex AI

GIT

Python

Job description

Cognizant is seeking a senior data scientist to design and deploy robust analytics and ML solutions in a hybrid work model. You will leverage Python, Vertex AI, BigQuery, and GIT to address complex retail and merchandising challenges across the enterprise.

Responsibilities include developing scalable models, building data pipelines, and deploying production workflows while ensuring data governance and stakeholder alignment. Strong collaboration with product teams and analysts is essential.