Credit Risk - Associate

At PwC, our people in data and analytics focus on leveraging data to drive insights and make informed business decisions. They utilise advanced analytics techniques to help clients optimise their operations and achieve their strategic goals.

In data analysis at PwC, you will focus on utilising advanced analytical techniques to extract insights from large datasets and drive datadriven decisionmaking. You will leverage skills in data manipulation, visualisation, and statistical modelling to support clients in solving complex business problems.

• Apply a learning mindset and take ownership for your own development.
• Appreciate diverse perspectives, needs, and feelings of others.
• Adopt habits to sustain high performance and develop your potential.
• Actively listen, ask questions to check understanding, and clearly express ideas.
• Seek, reflect, act on, and give feedback.
• Gather information from a range of sources to analyse facts and discern patterns.
• Commit to understanding how the business works and building commercial awareness.
• Learn and apply professional and technical standards (eg refer to specific PwC tax and audit guidance), uphold the Firms code of conduct and independence requirements.

• Candidate must have relevant experience in Machine Learning/Artificial Intelligence, Natural Language Programming, Statistical / Mathematical modeling, quantitative research, Fraud/AML risk management, or related Credit Risk field at a reputed bank, fintech, or a consulting firm. Wider skill requirements include
• Experience in Machine learning algorithms like Random Forest, SVM, Neural Network etc. and Artificial Learning use cases such as Natural Language Processing, Robotics etc. AML Scenario development, enhancement. Initial threshold setting and tuning.
• Proficiency in one or more analytical tools such Python, PY Spark, Data Science and cloudbased analytics etc.
• Experience in Model Development, Model Validation, Model Audit (implementation and execution experience will not be considered directly relevant)
• Business knowledge in transaction monitoring system, sanction screening, trade surveillance. Supports and/or runs global/regional internal SMEs; responsible for investigating and researching the Financial Crimes processes and tools identifying efficiency and effectiveness opportunities.
• Strengthen AML knowledge, Process Governance Controls. Support Regulatory exams, Internal Audit, Compliance Assurance test, Selfidentified issues / findings. Identify, assess, actively manage and control the risks that could come from our business, operational and organizational decisions.
• Develop and leverage innovative features and algorithms to drive down false positives and identify perceived threat across the firm
• Utilize traditional statistical analytics, graph theory / network science, ensemble methods and the like Natural language processing, text analytics, factors analysis / construct development and testing, machine learning feature development and engineering, etc.
• Prior experience in domains like AML/ Financial Crime analytics and strong knowledge of fraud data analysis and development, strategy design and delivery deployment.

Degrees/Field of Study required Degrees/Field of Study preferred Accepting Feedback, Accepting Feedback, Active Listening, Algorithm Development, Alteryx (Automation Platform), Analytic Research, Big Data, Business Data Analytics, Communication, Complex Data Analysis, Conducting Research, Customer Analysis, Customer Needs Analysis, Dashboard Creation, Data Analysis, Data Analysis Software, Data Collection, DataDriven Insights, Data Integration, Data Integrity, Data Mining, Data Modeling, Data Pipeline, Data Preprocessing, Data Quality