Senior Data Scientist (Consultant / Manager Level) – Visa

SEO Content & Metadata

• Post Title: Visa Senior Data Scientist Hiring 2026: ML, Distributed Systems, & Payments Analytics

• SEO Title: Visa Senior Data Scientist | Bengaluru | Machine Learning & Spark

• Meta Description:

Opportunity Overview

Technical & Project Lifecycle Workflow

The Senior Data Scientist drives end-to-end data products—from distributed data extraction to production ML/DL modelling and strategic business presentations:

┌───────────────────────────────────────────┐
│ 1. Data Ingestion & Extraction            │ ➔ Apache Spark, Hive/SQL, Hadoop Distributed Systems
└─────────────────────┬─────────────────────┘
                      ▼
┌───────────────────────────────────────────┐
│ 2. Scalable Modeling & Pipeline Design   │ ➔ Scikit-Learn, TensorFlow, PyTorch, GenAI / LLM Integration
└─────────────────────┬─────────────────────┘
                      ▼
┌───────────────────────────────────────────┐
│ 3. Quality & Reproducibility Controls    │ ➔ Reproducible Analytic Pipelines, Code Review, Governance
└─────────────────────┬─────────────────────┘
                      ▼
┌───────────────────────────────────────────┐
│ 4. Executive Delivery & Recommendations   │ ➔ Strategic Insights, Cross-Functional Alignment, Visualization
└───────────────────────────────────────────┘

Key Responsibilities

• Advanced Model Development: Build and deploy predictive ML, deep learning, recommendation, and generative models to solve complex payments and business challenges.

• Technical Project Leadership: Provide technical direction, scope project roadmaps, and lead data science teams across end-to-end execution cycles.

• Big Data Engineering & Pipelines: Query, aggregate, and process terabyte-scale datasets using PySpark, Hive, SQL, and Hadoop clusters.

• Reproducible Pipeline Engineering: Establish modular, reusable analytical pipelines and enforce code quality, versioning, and modeling rigor.

• Executive Communication & AI Productivity: Translate technical outputs into strategic decisions for cross-functional partners (Product, Engineering, Marketing) while incorporating Generative AI tools to accelerate workflows.

Technical & Qualification Requirements

Baseline Qualifications

• Education & Experience:

  • Bachelor’s degree + 5+ years of relevant data science experience OR

  • Advanced degree (Master’s, MBA, Ph.D.) + 2+ years of relevant experience.

• Core Skills: Hands-on experience building ML/DL models, creating reproducible data pipelines, querying distributed databases with SQL/Spark, and leveraging LLMs/GenAI tools.

Preferred Technical Skill Matrix

Application & Assessment Preparation

1. System Design & Distributed Data Focus: Practice designing scalable machine learning pipelines over distributed infrastructure (e.g., handling skewed data in Apache Spark joins).

2. Business Case Modeling: Be prepared to formulate machine learning metrics (e.g., Precision-Recall, AUC-ROC) in terms of financial metrics (e.g., fraud loss reduction, transaction approval rates).

3. Reproducibility & Coding: Demonstrate clean code practices, modular data pipeline architectures, and experience using Generative AI for code optimization.