Data Scientist

We are seeking a highly analytical and technically driven Financial Crime Advanced Analytics & AI Specialist to strengthen our financial crime detection, surveillance, and prevention capabilities. This role focuses on applying advanced analytics, machine learning, generative AI, and intelligence-led approaches to proactively identify, assess, and prevent financial crime risks across the organization.

The successful candidate will work closely with Financial Crime Surveillance (FCS) Operations, Compliance, Fraud teams, and Technology partners to deliver data-driven insights, scalable models, and strategic enhancements to financial crime frameworks.

Key Responsibilities

Threat Identification & Detection

• Conduct proactive threat hunting and emerging trend modelling to identify financial crime risks and system vulnerabilities.
• Perform portfolio, network, and behavioural analysis to identify suspicious activity related to money laundering, terrorist financing, fraud, and other financial crimes.
• Develop and apply advanced analytical techniques to detect anomalies, patterns, and hidden risks indicative of criminal activity.

Predictive Analytics

• Apply statistical modelling and machine learning algorithms to predict future risk scenarios and emerging financial crime threats.
• Build predictive models to support risk-based decision-making, investigative prioritisation, and resource allocation.

Machine Learning, AI & Advanced Analytics

• Leverage machine learning, generative AI, NLP, and Large Language Models (LLMs) to enhance detection accuracy and surveillance effectiveness.
• Design, build, test, and continuously refine models supporting real-time and near real-time monitoring of financial crime risks.
• Optimise surveillance strategies through advanced pattern recognition and automation.

Advanced Programming & Automation

• Utilise Python, R, SQL, and other advanced analytics tools to process large-scale datasets, develop algorithms, and deliver data-driven solutions.
• Develop scripts, models, and analytical tools that automate analysis, improve efficiency, and reduce operational effort.

Financial Crime Intelligence & Trend Analysis

• Monitor, analyse, and interpret current and emerging financial crime trends to stay ahead of evolving criminal methodologies.
• Apply intelligence harvesting techniques to collect, integrate, and analyse insights from multiple structured and unstructured data sources.
• Translate insights into actionable recommendations to inform strategy and policy.

Transaction Monitoring & Analytical Tooling

• Design, develop, and enhance transactional analysis tools for detecting money laundering, fraud, terrorist financing, and related risks.
• Support optimisation of detection rules, scenarios, and model-driven surveillance frameworks.

Continuous Improvement & Strategic Change

• Lead continuous improvement initiatives within financial crime surveillance and analytics processes.
• Identify opportunities for automation, optimisation, and strategic transformation.
• Provide thought leadership to evolve the organisation’s approach to financial crime detection and prevention.

Modelling & Risk Assessments

• Conduct risk assessments to support bulk activity prioritisation, including lookbacks and remediation exercises.
• Perform FTE and capacity modelling to assess staffing requirements and optimise investigative resources.
• Develop and enhance matching logic models (e.g., customer mastery, industry code or occupation matching) to improve risk accuracy and detection outcomes.

Data Quality & Controls

• Assess and validate upstream and Financial Crime Surveillance (FCS) data quality for accuracy, completeness, and consistency.
• Identify data quality gaps and partner with technology teams to resolve issues impacting analytics and detection performance.

Visualisation & Storytelling

• Develop clear and impactful visualisations, dashboards, and reports to communicate complex insights to non-technical stakeholders.
• Craft compelling data-driven narratives that inform executive decision-making and guide strategic financial crime initiatives.

Required Expertise & Skills

• 3–5 years of industry experience developing and deploying advanced analytics, machine learning, or generative AI solutions.
• Strong understanding of deep learning, reinforcement learning, and generative models, with a track record of successful deployment.
• Hands-on experience in:
• Natural Language Processing (NLP)
• Large Language Models (LLMs) & Generative AI
• OpenAI and/or Microsoft Cognitive Services (e.g., text mining, speech-to-text, image recognition)
• Machine Learning model development and optimisation
• Strong programming skills in Python and SQL.
• Experience with SAS and Power BI is highly desirable.
• Solid understanding of financial crime regulations and frameworks (AML, KYC, FATF).
• Excellent communication and collaboration skills with experience working in cross-functional teams.
• Demonstrated leadership capability, including mentoring or leading analytical teams.

Personal Attributes

• Analytical thinker with strong problem-solving skills
• Quick learner with intellectual curiosity
• Adaptable and comfortable working in a dynamic environment
• Strong collaborator and team player
• Strategic mindset with attention to detail
• Delivery-focused with a strong sense of ownership and accountability
• Passionate about continuous improvement and innovation

Key Working Relationships

• Financial Crime Surveillance (FCS) Operations Managers
• In‑Department Team Managers
• Financial Crime Compliance
• Group Technology & Data Teams
• Fraud and Scams Operations
• Group Customer Complaints
• Offshore Support Teams
• Internal and External Legal Stakeholders