Data Scientist

Data Scientist

The responsibilities of your position.

Threat Identification and Detection:

• Conduct threat hunting and emerging trends modelling to proactively identify financial crime risks and address

potential vulnerabilities.

• Perform portfolio analysis and network analysis to detect suspicious patterns and behaviours indicative of money

laundering, terrorist financing or other financial crimes.

• Develop and apply advanced analytics to detect anomalies and trends that could indicate criminal activity.

Predictive Analytics:

• Use statistical methods and machine learning algorithms to predict future outcomes and identify emerging financial

crime threats.

• Develop predictive models to inform risk-based decision-making and resource prioritisation for investigations.

Machine Learning & Artificial Intelligence:

• Apply machine learning techniques, generative AI, Natural Language Processing and Large Language Models to

improve identification of patterns and optimise surveillance strategies.

• Build, test, and refine predictive models to support real-time financial crime monitoring and investigation.

Advanced Programming Skills:

• Utilise programming languages such as Python, R, and other advanced tools (instead of SQL and Excel) to analyse

large datasets, develop algorithms, and implement data-driven solutions.

• Develop tools and scripts to automate data analysis and improve operational efficiency.

Financial Crime Trends and Intelligence Harvesting:

• Analyse and interpret current financial crime trends to stay ahead of evolving criminal tactics.

• Leverage intelligence harvesting techniques to collect and analyse data from multiple sources to inform strategy.

Emerging Trend Analysis:

• Continuously monitor and evaluate emerging financial crime trends.

• Provide actionable insights and recommendations based on data-driven analysis of new and evolving threats.

Transactional Analysis Tooling:

• Develop and enhance tools for analysing transactional data, focusing on the identification of financial crimes such as

money laundering, fraud, and terrorist financing.

Continuous Improvement and Strategic Change:

• Lead continuous improvement efforts within the financial crime surveillance processes, identifying areas for

optimisation and automation.

• Highlight opportunities for strategic change in the organisation’s approach to financial crime detection and prevention.

Modelling and Risk Assessments:

• Risk assessments to inform prioritisation for "bulk activity" such as lookbacks and remediation actions.

• FTE modelling to assess staffing needs and optimise resource allocation for surveillance and investigations.

• Develop matching logic models, such as customer mastery, industry code/occupation matching, to refine risk

assessments and improve detection accuracy.

Data Quality Analysis:

• Analyse both upstream and Financial Crime Surveillance (FCS) generated data quality to ensure accuracy and

completeness.

• Identify and resolve data quality issues that could impact the effectiveness of financial crime detection efforts.

Visualisation and Storytelling:

• Create compelling visualisations to communicate complex findings and insights to non-technical stakeholders.

• Craft data-driven narratives that inform decision-making and guide strategic initiatives within the organization

EXPERTISE

3 to 5 years of industry experience in developing and deploying

generative models for various applications, with a strong track record

of delivering successful AI solutions.

• Deep understanding of the latest advancements in deep learning,

reinforcement learning and generative models, with a proven track

record of building and deploying successful applications/solutions in

this field is desirable.

• Experience in the following areas: natural language processing, or

computer vision Methods: NLP, LLM, GenAI, Open AI, Microsoft

Cognitive Services – Audio to text, text mining NLP, Image recognition,

Machine Learning

• Strong programming skills in Python and SQL skills.

• Strong experience in SAS and PowerBI is desirable.

• Strong communication and collaboration skills, with the ability to work

effectively with cross-functional teams.

• Proven ability to do mentorship or leading the team(s). Familiarity with

industry standards and regulatory frameworks related to financial crime

prevention (e.g., AML, KYC, FATF).

PERSONAL ATTRIBUTES

• Quick learner and problem solver

• Analytical Mindset

• Adaptability and Flexibility

• Collaboration and Teamwork

• Strategic Thinking

• Driven to deliver - proven ability to deliver the best possible results

for the organisation; and shows determination, resourcefulness,

and a sense of purpose in achieving this

• Strong drive for continuous improvement

WORKING RELATIONSHIPS

FCS Operations Managers

• In-Department Team Managers

• Financial Crime Compliance

• Group Technology & Data teams

• Fraud and Scams Operations

• Group Customer Complaints

• Offshore Support

• Internal and External Lega