Associate AI Engineer / Data Scientist

Line of Service Assurance Industry/Sector FS X-Sector Specialism Assurance Management Level Associate Job Description & Summary At PwC, we help clients build trust and reinvent so they can turn complexity into competitive advantage. We’re a tech-forward, people-empowered network with more than 364,000 people in 136 countries and 137 territories. Across audit and assurance, tax and legal, deals and consulting, we help clients build, accelerate, and sustain momentum. Find out more at . Our Financial Services Assurance Practice works with organisations to strengthen trust and transparency by building, maintaining and providing trust over financial reporting in a fast changing, technology-driven world. As Asia’s top financial services practice, our audit approach is at the leading edge of best practice. We draw upon our extensive industry knowledge for our clients including top blue chip companies in the asset management, banking, capital markets, and insurance sectors. We provide our clients with insights, empowered by leading technologies, into marketplace developments and global opportunities. As an Associate AI Engineer / Data Scientist, you will work as part of the Financial Services Assurance Digital Innovation Garage to design, build and support practical data, analytics and AI-enabled automation solutions. You will help translate business and audit challenges into data-driven use cases, prepare and analyse datasets, develop machine learning or AI-assisted prototypes, and support senior team members in testing, documenting and operationalising solutions in a responsible and controlled manner. Develop, test and maintain data analytics, machine learning and AI-assisted automation solutions using tools such as Python, SQL, Power BI, Power Automate, Alteryx or similar technologies. Perform data extraction, cleansing, transformation, validation and exploratory analysis to support audit, assurance and internal innovation use cases. Support the development of AI and machine learning prototypes, including feature engineering, model evaluation, prompt engineering, retrieval-augmented generation concepts and responsible use of large language models where applicable. Collaborate with audit, risk, technology and innovation teams to understand requirements, clarify problem statements and convert ideas into workable proof-of-concepts or production-ready components. Document assumptions, data handling steps, testing results, limitations and user guidance in a clear and structured manner. Demonstrate critical thinking, curiosity and a willingness to learn new technologies while bringing structure to ambiguous or unstructured problems. Communicate progress, risks, blockers and outcomes confidently to senior team members and stakeholders. Uphold the firm’s code of ethics, business conduct, data protection expectations and responsible AI principles. Minimum Years Of Experience 1–3 years of relevant experience in data science, AI engineering, machine learning, analytics engineering, intelligent automation or technology-enabled process improvement. Candidates with strong internship, academic or project-based exposure in AI/ML and data analytics may also be considered. Preferred Knowledge/Skills Demonstrates thorough abilities and/or a proven record of success as a team member including the following areas Hands-on experience with Python and common data science libraries such as pandas, NumPy, scikit-learn or similar tools; familiarity with R will be considered a plus. Working knowledge of SQL, data modelling, data cleansing, data validation, feature engineering and exploratory data analysis. Basic to moderate understanding of machine learning concepts, including supervised and unsupervised learning, model evaluation, overfitting, regression, classification, clustering and predictive modelling. Exposure to Generative AI, large language models, prompt engineering, embeddings, vector search, retrieval-augmented generation or AI agent concepts will be an advantage. Experience with analytics, automation or visualisation platforms such as Power BI, Tableau, Alteryx, Power Automate, UiPath, ABBYY OCR or similar tools. Ability to support solution design, requirements gathering, user acceptance testing, documentation and handover for data, AI and automation projects. Good understanding of data governance, data privacy, data quality, access controls and responsible use of AI in a professional services or regulated environment. Good to have: basic understanding of finance systems, general accounting concepts, ledgers, sub-ledgers, finance data warehouses, audit processes or financial services datasets. Degrees/Field Of Study Required Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, Engineering, Information Systems, Business Analytics, Quantitative Finance, Accounting Analytics or a related quantitative / technology discipline. Relevant postgraduate qualifications, professional certifications or strong project-based experience in AI, machine learning, analytics, automation or software engineering will be considered an advantage. Education (if blank, degree and/or field of study not specified) Degrees/Field of Study required: Degrees/Field of Study preferred: Certifications (if blank, certifications not specified) Required Skills Optional Skills Accepting Feedback, Access Control (AC), Active Listening, Audit Internal Controls, BASIS Database Management System (DBMS), Business Process Improvement, Communication, Emotional Regulation, Empathy, ERP System Integration, Inclusion, Intellectual Curiosity, IT Controls, Optimism, Process Control, SAP ERP, SAP Governance, Risk and Compliance (GRC), SAP HCM, SAP Supply Chain Management (SAP SCM), Security Control, Security Control Assessment, SOX Compliance, Teamwork, Well Being Desired Languages (If blank, desired languages not specified) Travel Requirements Not Specified Available for Work Visa Sponsorship? Yes Government Clearance Required? Yes Job Posting End Date