National Delivery Excellence- QA AI Tester- Senior Associate

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.

Skills

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 Firms code of conduct, and independence requirements.
  
  

The Opportunity

When you join PwC Acceleration Centers (ACs), you step into a pivotal role focused on actively supporting various Acceleration Center services, from Advisory to Assurance, Tax and Business Services. In our innovative hubs, you’ll engage in challenging projects and provide distinctive services to support client engagements through enhanced quality and innovation. You’ll also participate in dynamic and digitally enabled training that is designed to grow your technical and professional skills.

As part of the Data and Analytics Engineering team you will lead quality assurance efforts for AI-driven products and machine learning systems. As a Senior Associate you will analyze complex problems, mentor others, and maintain elevated standards while building meaningful client connections and navigating increasingly complex situations. This role offers the chance to deepen your technical knowledge and grow your personal brand while collaborating with cross-functional teams to secure the reliability and performance of innovative AI solutions.

Responsibilities

• Lead quality assurance initiatives for AI-driven products
• Analyze intricate problems and provide impactful solutions
• Mentor junior team members to enhance their skills
• Maintain exceptional standards in project deliverables
• Build and nurture meaningful client relationships
• Collaborate with cross-functional teams to provide product reliability
• Enhance technical knowledge in machine learning systems
• Navigate complex situations to drive project success
  
  

What You Must Have

• Bachelors Degree in Computer Science, Data Science, or related field
• 4 years of experience in software testing or quality engineering
• Oral and written proficiency in English required
  
  

What Sets You Apart

• Python, JavaScript, or Java, with experience using PyTest, Robot Framework
• Strong understanding of AI/ ML,
• ML platforms such as TensorFlow, PyTorch, MLflow, or SageMaker,
• MLOps tools (Docker, Kubernetes, Airflow, Jenkins, Azure ML, etc.) and data validation frameworks (Great Expectations, Pandera, etc.),
• Automation testing tools (Selenium, Playwright, Cypress) and API testing (Postman, REST Assured), Gen AI, NLP Models or LLM

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