Assistant Manager-Data Science at WNS Global Services

Gurgaon, haryana, India -

Full Time

Start Date

Immediate

Expiry Date

12 Jul, 26

Salary

0.0

Posted On

13 Apr, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Data Engineering, Data Automation, Data Pipelines, Pandas, NumPy, Requests, BeautifulSoup, Selenium, Git, API Integration, Web Scraping, Workflow Orchestration, Data Quality, Data Governance, Error Handling

Industry

Business Consulting and Services

Description

Company Description WNS, part of Capgemini, is an Agentic AI-powered leader in intelligent operations and transformation, serving more than 700 clients across 10 industries, including Banking and Financial Services, Healthcare, Insurance, Shipping and Logistics, and Travel and Hospitality. We bring together deep domain excellence – WNS’ core differentiator – with AI-powered platforms and analytics to help businesses innovate, scale, adapt and build resilience in a world defined by disruption.Our purpose is clear: to enable lasting business value by designing intelligent, human-led solutions that deliver sustainable outcomes and a differentiated impact. With three global headquarters across four continents, operations in 13 countries, 65 delivery centers and more than 66,000 employees, WNS combines scale, expertise and execution to create meaningful, measurable impact. Job Description Location : Gurgaon Hybrid working UK time zone Exp : 5+ Yrs Skills and Experience Must-Have Skills 5+ years of hands-on experience in Data Engineering, Data Automation, or Data Pipeline development. Strong Python expertise with experience building and maintaining production-grade data pipelines. Data ingestion experience using APIs, web scraping, and file-based sources (Excel, HTML, JSON, XML). Strong working knowledge of Pandas, NumPy, Requests, BeautifulSoup, Selenium. Solid understanding of data pipeline architecture, modular design, reusability, and performance optimization. Experience with workflow orchestration concepts such as scheduling, dependencies, retries, and monitoring. Hands-on experience with logging, alerting, error handling, and monitoring for production workflows. Proficiency with Git and strong coding, testing, and documentation standards. Strong understanding of data quality, validation, governance, and schema evolution. Ability to troubleshoot and resolve production data pipeline issues independently. Nice-to-Have Skills Experience with Decisions or similar workflow automation platforms. Exposure to PySpark or distributed data processing frameworks. Experience working with on-premise enterprise data ecosystems. Experience mentoring junior engineers or influencing engineering best practices. VBA Key Responsibilities Design and develop scalable, reusable data collection systems using APIs, web scraping, and file-based ingestion (e.g., Excel, HTML, JSON). Lead development of Python-based data ingestion and automation methods for diverse data sources. Integrate ingestion pipelines with enterprise data storage and processing layers, ensuring reliability, performance, and maintainability. Ensure data quality, governance, and cross-system consistency in collaboration with backend, analytics, and UI teams. Productionize Python-based analytical models for scalable, reliable execution in operational environments. Enable end-to-end automation of data pipelines and workflows with minimal manual intervention. Establish and enforce development standards including code structure, testing, documentation, logging, and error handling. Contribute to internal workflow orchestration and Decisions-based transformation solutions. Technical Environment Python-based custom data pipeline framework API-driven data access and web scraping Decisions workflow automation (nice to have) Internally managed workflow orchestration services Qualifications Bachelor’s degree in Computer Science, Information Technology, Engineering, Mathematics, or a related fieldMaster’s degree in a relevant discipline is preferred but not mandatoryEquivalent practical experience in data engineering or data automation will be considered in lieu of formal education

Responsibilities

Design and develop scalable data collection systems using APIs and web scraping to automate data ingestion. Lead the productionization of Python-based analytical models while ensuring data quality and cross-system consistency.