Risk Analytics Full Stack Engineer

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. 

Full stack Engineer

About Global Delivery Services

Global Delivery Services refers to EY's worldwide network of service delivery centres. The GDS team plays an important role in EY’s strategy by ensuring effective support to EY’s growth agenda.

Client service is focused on providing Consulting, Assurance, Tax, Strategy & Transactions, and Knowledge support to our clients around the world. The teams enable account teams worldwide to provide seamless, high-quality, value-added support, helping deliver exceptional client service.

Enablement Services provides cost-effective, high-skilled, and innovative services to support EY’s global and local enablement teams. Markets, BMC, AWS, Finance and Accounting, Risk Management, Procurement, People Shared Services, IT Service Delivery and IT Global Infrastructure services, are among the services offered by Enablement Services.

Our innovation specialists serve the GDS Client Service and Enablement Services teams, along with Service Lines, Core Business Services and Sectors. The team brings the desired environment, technologies and skilled teams together for facilitation, rapid prototyping and innovative thinking. The competencies offered include analytics, digital, user experience, mobile technology, infrastructure, Microsoft technologies and open innovation.

The Opportunity

Full stack Engineer

Your Key Responsibilities

• Being passionate to work as part of a next generation product, ready to be agile and have innovation mindset
• Implement adopted design/development methodologies, patterns, and toolsets.
• Understanding business objectives and designing models that help to achieve them, along with metrics to track their progress
• Managing available resources such as hardware, data, and personnel so that deadlines are met
• Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world
• Verifying data quality, and/or ensuring it via data cleaning.
• Design and develop machine learning algorithms.  
• Discover, design, and develop analytical methods to support novel approaches of data and information processing.