Financial Crime has become a major issue for all financial services organisations as well as many others caught up in its scope. The compliance focus and the associated costs are substantial, and at all levels the penalties for failure have become ever more significant.
PwC's Financial Crimes Unit (FCU) is structured to reflect the multidisciplinary financial crime task forces established by major financial institutions and government agencies. Globally, we are comprised of more than 3000 professionals in anti-money laundering, cybersecurity, sanctions, fraud and anti-bribery/corruption. We offer the full breadth of PwC’s technology, regulatory and investigative experience from its financial services and forensics advisory groups. The FCU advises the largest global and US institutions across the banking, capital markets, asset management and insurance industries. We work with clients to provide innovative and advanced solutions for responding to Financial Crime incidents, remediating past deficiencies, developing enhanced operating models, and optimising transaction monitoring systems.
We pride ourselves on our strong supportive team culture and diverse mix of backgrounds. We also strongly believe in the importance of potential and desire to learn. As such it is important to us that you fit well within our team, have a strong desire to learn, develop your own skills and also support and coach others.
In return for joining our team, you will gain exposure to a wide range of industry clients, and be rewarded with a career built on a variety of experiences, and the opportunity for senior leadership development within our firm. We will also provide you the opportunity to develop a global network of contacts across PwC who will support you in your growth as a leader, consultant and analytics professional.
As part of the Financial Crime Unit’s expansion plan across South East Asia Consulting, we are actively hiring different levels of talent to join us in working with our top tier clients on various Forensic and Financial Crime related engagements.
We have opportunities for professionals with a wide range of experience and want to hear from people with between 2-5 years’ experience in this field. Your experience may have been gained within a Banking or Consultancy or an alternate industry and should include elements as described below (don’t be discouraged if you don’t possess all or even most of them, we would still like to talk to you about joining our team if you have some applicable professional experience).
About The Role
- 2-5 years of relevant experience in data science and analytics with the financial services industry (banking, insurance, asset management, fin-tech)
- Familiarity with SQL, R, Python, PySpark and knowledge of machine learning libraries would be useful (pandas, mlib)
- Knowledge of machine learning toolkits such as H2O, TensorFlow, KubeFlow
- Understanding of the various algorithms for unsupervised, supervised, ensemble and re-inforcement learning would be useful and the ability to discern why.
- A good grasp of statistics, such as clustering, segmentation, regression, standard deviations, r-squared, moving averages techniques, various sampling techniques, boosting learning labels
- Familiarity with reporting tools such as PowerBI, Tableau, Klik would be useful as well as strong Excel skills
- Data preparation skills, one-hot encoding, pipelining, data quality improvement techniques, data lineage, removing noise, feature relevance
- Supporting development, testing and deployment of system initiatives
- A desire and ability to think abstractly, solve problems proactively and deal with ambiguity
- The agility to adapt and apply new and varied analytical solutions to client opportunities that may arise
- A passion to work collaboratively with diverse, international teams
- Enthusiasm and drive to be a leader in our team, helping shape and develop its future
- Strong communication, presentation, project management and time management skills
- Financial Crime background at a bank, consulting firm, fin-tech/reg-tech, insurer etc, covering Fraud, Sanctions and/or Transaction Monitoring
- Experience with model validation and tuning (reducing false positives) for transaction monitoring, banking & payment/insurance fraud and/ screening systems (sanctions, watchlists, adverse media, name)
- Rules design for transaction monitoring, fraud or sanctions
- ML feature engineering for transaction monitoring, fraud or sanctions
- Providing data analysis and project governance expertise to support delivery including, writing reports and technical specifications
- University degree in a related discipline (e.g. Computer Science, Mathematical Science, Financial Engineering, Actuarial, Statistics etc.)
- Practical experience with using machine learning techniques in an industry, with programming skills
- Analytical and independent thinker with strong English written and verbal communication skills
- Willingness to travel around the region for an extended period (subject to Covid rules)