At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven decision making. You will work on developing predictive models, conducting statistical analysis, and creating data visualisations to solve complex business problems. Focused on relationships, you are building meaningful client connections, and learning how to manage and inspire others. Navigating increasingly complex situations, you are growing your personal brand, deepening technical expertise and awareness of your strengths. You are expected to anticipate the needs of your teams and clients, and to deliver quality. Embracing increased ambiguity, you are comfortable when the path forward isn’t clear, you ask questions, and you use these moments as opportunities to grow. Examples Of The Skills, Knowledge, And Experiences You Need To Lead And Deliver Value At This Level Include But Are Not Limited To: Respond effectively to the diverse perspectives, needs, and feelings of others. Use a broad range of tools, methodologies and techniques to generate new ideas and solve problems. Use critical thinking to break down complex concepts. Understand the broader objectives of your project or role and how your work fits into the overall strategy. Develop a deeper understanding of the business context and how it is changing. Use reflection to develop self awareness, enhance strengths and address development areas. Interpret data to inform insights and recommendations. Uphold and reinforce professional and technical standards (e.g. refer to specific PwC tax and audit guidance), the Firm's code of conduct, and independence requirements. Responsibilities Design, develop, and maintain high-performance backend applications using Python, with a focus on scalability, maintainability, and system integration. Build complex and optimized SQL queries, procedures, and data models to support business-critical functions across multiple systems. Lead integration efforts using RESTful APIs, cloud services, and messaging frameworks to connect various platforms and applications. Deploy AI/ML models and services using Azure Machine Learning and integrate them into production systems through RESTful APIs. Architect and implement enterprise-grade workflows and orchestration solutions leveraging Azure services such as Data Factory, Azure Functions, Logic Apps, and Azure Storage. Enforce best practices in development lifecycle including version control, automated testing, CI/CD pipelines, and documentation. Collaborate with data engineers, architects, and business stakeholders to deliver end-to-end data and AI solutions aligned with business goals. Knowledge And Experience Extensive hands-on experience in Python development, including advanced programming concepts, system design, and integrations. Deep expertise in SQL, with a proven ability to write optimized, scalable queries and manage complex relational database operations. Strong integration background, including working with RESTful APIs, databases (SQL/NoSQL), message queues, and cloud services. Practical experience with Azure cloud ecosystem, particularly in deploying serverless applications, orchestrating workflows, and managing data pipelines. Familiarity with AI/ML model integration and deployment, with the ability to support full lifecycle implementation from a systems perspective. Solid understanding of ALM and DevOps practices, including source control, CI/CD, testing, and environment management. Skills Advanced Python development skills for AI, ML, and data engineering use cases. Strong analytical thinking and problem-solving abilities. Excellent communication skills and ability to collaborate across technical and non-technical teams. Ability to manage multiple projects in a fast-paced, dynamic environment. Familiarity with AI governance, responsible AI principles, and secure cloud-native solution architecture on Azure. Show more Show less
