Company Description
WNS (Holdings) Limited (NYSE: WNS), is a leading Business Process Management (BPM) company. We combine our deep industry knowledge with technology and analytics expertise to co-create innovative, digital-led transformational solutions with clients across 10 industries. We enable businesses in Travel, Insurance, Banking and Financial Services, Manufacturing, Retail and Consumer Packaged Goods, Shipping and Logistics, Healthcare, and Utilities to re-imagine their digital future and transform their outcomes with operational excellence.We deliver an entire spectrum of BPM services in finance and accounting, procurement, customer interaction services and human resources leveraging collaborative models that are tailored to address the unique business challenges of each client. We co-create and execute the future vision of 400+ clients with the help of our 44,000+ employees.
Job Description
Technical Manager for Cloud-Based Data Science Program
Role Overview:
We are looking for an experienced Technical Manager to oversee a cloud-based data science program, guiding a team of data engineers, data scientists, and cloud architects to deliver scalable, efficient, and high-performance data science solutions.
The Technical Manager will be responsible for ensuring the successful execution of the program, managing the technical aspects of the project lifecycle, and collaborating across teams to drive results.
Key Responsibilities:
- Team Leadership & Management:
o Lead, mentor, and inspire a cross-functional team of engineers, data scientists, and other technical professionals.
o Foster a collaborative and innovative environment where team members can grow and achieve their full potential.
o Manage day-to-day operations and project deliverables, ensuring timely and high-quality outputs.
- Cloud Architecture & Data Science Strategy:
o Oversee the design, development, and optimization of cloud-based platforms for data science and machine learning.
o Collaborate with senior stakeholders to define technical strategies and ensure alignment with business objectives.
o Ensure the architecture supports scalability, performance, data security, and efficient integration of machine learning models and analytics.
o Lead the planning, execution, and delivery of cloud-based data science programs.
o Define project timelines, milestones, and budgets while managing risks and ensuring project scope is met.
o Coordinate with product managers, business analysts, and other stakeholders to prioritize tasks and ensure the delivery of business value.
- Cross-Functional Collaboration:
o Work closely with other technical and business teams to align objectives, integrate data workflows, and ensure smooth deployment of data-driven solutions.
o Partner with cloud engineers, data scientists, and machine learning experts to ensure technical excellence across the program.
o Act as a bridge between technical teams and non-technical stakeholders to ensure alignment on project goals.
- Technical Excellence & Best Practices:
o Establish and enforce best practices for cloud architecture, data pipelines, and model deployment within the data science program.
o Monitor the performance and reliability of cloud-based systems, ensuring continuous optimization and high availability.
o Stay up-to-date with industry trends and emerging technologies in cloud computing, data science, and machine learning.
- Performance & Quality Assurance:
o Ensure the high quality of code, models, and data pipelines through proper code reviews, testing, and monitoring.
o Implement robust monitoring and logging systems to detect and address issues proactively.
o Continuously evaluate the efficiency and effectiveness of the data science program and recommend improvements.
- Resource & Budget Management:
o Oversee the allocation of resources (personnel, cloud infrastructure, tools) and ensure they are used efficiently to meet program goals.
o Manage budgets related to cloud services, data engineering tools, and infrastructure investments.
- Stakeholder Reporting & Communication:
o Provide regular updates to senior management and stakeholders on the progress of the program, risks, and roadblocks.
o Create and deliver presentations, reports, and dashboards to communicate technical findings and achievements.
Qualifications
Required Qualifications:
o Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
o Minimum 6+ years of experience in a technical leadership or management role, ideally in cloud-based data science or machine learning programs.
o Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud.
o Proven track record of managing end-to-end data science or machine learning projects in cloud environments.
o Experience in leading teams of engineers and data scientists, with a strong understanding of cloud architecture, data pipelines, and model deployment.
o Strong understanding of cloud infrastructure, data engineering, and machine learning technologies.
o Proficiency in programming languages such as Python, Java, SQL, or Scala.
o Familiarity with cloud services like AWS S3, Redshift, BigQuery, Databricks, and Google Cloud AI/ML tools.
o Experience with containerization tools like Docker and orchestration platforms like Kubernetes.
o Knowledge of DevOps practices, CI/CD pipelines, and tools (e.g., Jenkins, Terraform).
- Leadership & Communication Skills:
o Strong leadership abilities with experience managing diverse technical teams and driving results.
o Excellent verbal and written communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
o Strong interpersonal skills to collaborate with cross-functional teams and manage stakeholder expectations.
Preferred Qualifications:
- Experience with managing multi-cloud or hybrid-cloud architectures.
- Familiarity with big data technologies like Hadoop, Spark, or Kafka.
- Advanced certifications in cloud platforms (e.g., AWS Certified Solutions Architect, Azure Solutions Architect Expert).
- Background in developing data governance strategies and ensuring compliance with data privacy regulations.
- Experience with business intelligence tools (e.g., Tableau, Power BI) and data visualization best practices.
- Agile knowledge
