WNS
Website:
wns.com
Company:
https://www.linkedin.com/company/wns-global-services
Seniority: Mid-Senior level
Industries: Outsourcing/Offshoring
Job details:
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
Role Summary
The Technical Manager is responsible for leading a cloud-based data science program and managing a cross-functional team of data engineers, data scientists, cloud architects, and technical professionals. The role oversees the end-to-end delivery of scalable, secure, and high-performance cloud solutions while ensuring alignment with business objectives. The successful candidate will drive technical strategy, manage project execution, collaborate with stakeholders, and ensure the delivery of high-quality data science and machine learning solutions.
Key Responsibilities
Lead, mentor, and manage a multidisciplinary technical team, fostering collaboration, innovation, and continuous learning.
Oversee the design, development, and optimization of cloud-based data platforms, machine learning solutions, and data pipelines, ensuring scalability, performance, security, and reliability.
Manage the complete project lifecycle, including planning, execution, resource allocation, risk management, timelines, budgets, and successful delivery.
Collaborate with product managers, business analysts, and technical stakeholders to prioritize work, align technical solutions with business goals, and ensure seamless deployment of cloud-based analytics and machine learning solutions.
Establish and promote best practices for cloud architecture, software development, data engineering, model deployment, code quality, testing, monitoring, and operational excellence.
Continuously evaluate system performance, recommend improvements, and stay current with emerging technologies in cloud computing, artificial intelligence, machine learning, and data engineering.
Manage cloud infrastructure, tools, and budgets efficiently while providing regular project updates, dashboards, presentations, and technical reports to leadership and stakeholders.
Act as the primary liaison between technical teams and business stakeholders, ensuring clear communication and effective stakeholder management.
Qualifications
Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related discipline.
Minimum 6 years of experience in technical leadership or management, preferably leading cloud-based data science, analytics, or machine learning programs.
Hands-on experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform and a proven track record of delivering end-to-end cloud-based data science solutions.
Strong knowledge of cloud infrastructure, data engineering, machine learning, cloud architecture, data pipelines, and model deployment.
Proficiency in Python, Java, SQL, or Scala.
Experience with cloud services such as AWS S3, Redshift, BigQuery, Databricks, Google Cloud AI/ML services, Docker, Kubernetes, CI/CD pipelines, Terraform, and Jenkins.
Excellent leadership, project management, communication, stakeholder management, and problem-solving skills with the ability to explain complex technical concepts to both technical and non-technical audiences.
Preferred Qualifications
Experience managing multi-cloud or hybrid cloud environments. Knowledge of Hadoop, Spark, Kafka, and other big data technologies.
Cloud certifications such as AWS Certified Solutions Architect or Microsoft Azure Solutions Architect Expert are preferred.
Experience with data governance, data privacy compliance, business intelligence tools including Tableau and Power BI, data visualization best practices, and Agile methodologies is an advantage.
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