Dr Hassan Saif

Senior Data Science Manager

2022 - Present

HSBC Group, Financial Crime Digital Enablement, Chief Compliance Office, London, UK

I head a multinational team of data scientists, software engineers and system architects to develop cutting-edge, scalable AI-powered solutions within the HSBC’s Financial Crime Digital Enablement function. My role involves leveraging advanced technologies such as NLP, GenAI and Large Language Models to optimise and automate manual complex processes such as model and policies documentation and financial crime investigations. I manage stakeholder relationships across various sectors, including financial crime, model risk, and name screening, ensuring effective communication, collaboration, and presentation of complex technical developments in AI and ML to stakeholders in a clear manner. Key responsibilities include:

• Leading the design and development of AI-driven solutions to streamline HSBC's manual ML models and policies documentation and review processes.
• Implementing Agile methodologies for product and team management, ensuring prompt delivery and adaptability to evolving requirements.
• Facilitating collaboration among IT, Analytics, and Software Engineering functions to ensure that the team’s deliverables are in line with the organisation’s strategic initiatives in financial crime prevention and risk management.
• Overseeing the complete project management lifecycle for our team’s cloud-based ML solutions, from gathering requirements to deployment, while effectively handling stakeholder and client interactions to ensure project objectives meet business needs and expectations.

Senior Data Scientist

2018 - 2022

HSBC Group, Research & Development, Chief Compliance Office, London, UK

Lead the design and development of innovative, large-scale, and production-ready anti-financial crime systems. This is an interdisciplinary role at the intersection of quantitative analysis/ML, software development, and pipeline infrastructure engineering. I am also responsible for managing multiple cross-jurisdiction analytics teams, as well as preparing and delivering a broad range of technical briefings for a range of senior HSBC executives. Key responsibilities and achievements include:

• Managing the design, development, and delivery of HSBC's global anti-financial crime ML-based ecosystems.
• Working with extremely large, complex data sets (+30Bn records), generating insights, and identifying and modelling historical and new emerging financial crime risk behaviours and trends.
• Building an end-to-end NLP-powered ML pipeline for identifying confirmed financial crime risk typologies within a global population of the HBSC known-bad actors. The developed pipeline helped deliver auto-generated gold-standard evaluation datasets for model training and evaluation.
• Building ML-based anti-money laundering models that are ~5 times better and detect risk ~0.7 months faster than existing systems.
• Working jointly with IT and engineering teams on the integration and deployment of large-scale ML pipelines.
• Leading model performance analysis and evaluating business metrics to generate actionable insights and provide compelling visualisations for both business and technical stakeholders to support evidence-based decision-making processes within the bank.
• Managing key stakeholder relationships to determine and translate business needs into research requirements and development workflows.

Lead Data Scientist

2017 - 2018

True212, Engineering Dept, , London, UK

Led the design and development of project RER (Real-Time Editorial Resource), a recommender system that provides journalists and editors with real-time insights, recommendations, and monitoring services on social media. Main responsibilities included:

• Design scalable ML and NLP pipelines (NER, WSD, Topic Modelling, and Semantic Vector Representations) for identifying relevant insights, trends, and breaking news from social media.
• Using Big Data technologies and event streaming frameworks (Hadoop, Spark, Kafka) along with cloud computing services (AWS, Atlas) to design and build an event-driven and fault-tolerant system architecture for the RER's ML framework.
• Managing the RER software development lifecycle across internal and overseas teams.

ML Research Associate

2015 - 2017

The Open University, , Knowledge Media Institue, Milton Keynes, UK

Led and managed two EU-funded research and innovation projects: COMRADES and TRIVALENT:

• In TRIVALENT, I worked closely with over 20 research teams and policing organizations across Europe to target the problem of online radicalisation and counter-terrorism on social media. This involved leveraging techniques from ML, User Behavior Analysis, and Big Data to build a large-scale ML framework for detecting radicalized users on social media, tracking the divergence of user behaviours during the radicalisation process, and analyzing what influences users to adopt a radicalized stance. Evaluation of over 100 million tweets showed that the developed models produce ~7% better accuracy than existing models.
• In COMRADES, I designed a semantically enriched Deep Learning model for crisis-related event detection on social media. The developed model showed to outperform existing ML models by up to 3.7%. This model is currently used by multiple international humanitarian organizations to identify crisis-related information in social streams

Research Assistant

2014 - 2015

The Open University, , Knowledge Media Institue, Milton Keynes, UK

Leveraged NLP, Machine Learning, Topic Modelling, and Semantic Web techniques to develop prediction models to capture the dynamics of citizen's policy discussions and the spread of polarised sentiment on social media. These models have been used by a wide range of political scientists and governmental organisations (including Members of Parliament in both Germany and the UK) as part of the EU-funded project SENSE4US.

Software Engineer (PT)

2013 - 2013

The Open University, , Knowledge Media Institue, Milton Keynes, UK

Developed a web-based framework for assessing the quality of research publications within the Open University (OU) as part of the UK Research Excellence Framework (REF). This framework has been continuously used to assess the research performance of the OU and help aid the research grant allocations from multiple Higher Education funding bodies. Programmed in PHP, JavaScript, MySQL, and SPARQL.