Financial Crime Data Scientist | S3 | D-FACT | Multiple Locations | Internal Mobility

Financial Crime Data Scientist | S3 | D-FACT | Multiple Locations | Internal Mobility Country: United Kingdom IT STARTS HERE Santander ( www.santander.com ) is evolving from a global, high-impact brand into a technology-driven organisation , and our people are at the heart of this journey. Together , we are driving a customer-centric transformation that values bold thinking, innovation , and the courage to challenge what’s possible. This is more than a strategic shift. It’s a chance for driven professionals to grow, learn, and make a real difference . Our mission is to contribute to help more people and businesses prosper . We embrace a strong risk culture and all our professionals at all levels are expected to take a proactive and responsible approach toward risk management. Retail & Commercial Banking is a global business integrating all our retail and commercial banking activities to better serve our customers, improve efficiency and drive value creation. THE DIFFERENCE YOU MAKE Santander UK is looking for a Financial Crime Data Scientist based out of Milton Keynes or London . To design, develop and continuously enhance advanced analytical solutions that strengthen Santander's Financial Crime capability, with a primary focus on Transaction Monitoring. The role will leverage data science, machine learning and advanced analytics to identify emerging financial crime risks, improve detection effectiveness and support the delivery of robust, efficient and regulatory compliant Financial Crime controls. We’re shaping the way we work through innovation, cutting-edge technology, collaboration and the freedom to explore new ideas. To succeed in this role, you will be responsible for: Design, develop and maintain Transaction Monitoring models and analytical solutions to detect financial crime risks. Analyse large and complex datasets to identify patterns, anomalies and emerging financial crime typologies. Apply statistical modelling, machine learning and data mining techniques to solve Financial Crime use cases. Support the end to end model development lifecycle, including data exploration, feature engineering, model development, testing, validation and implementation. Partner with Financial Crime SMEs, Technology, Data Engineering and Risk teams to translate business requirements into analytical solutions. Monitor model performance and recommend enhancements to improve effectiveness, efficiency and customer outcomes. Produce high quality technical documentation to support governance, model validation and regulatory requirements. Present analytical findings and recommendations to both technical and non technical stakeholders. Contribute to the continuous improvement of analytical methodologies, tools and best practices across the Financial Crime Controls, Models & Analytics team. Ensure analytical solutions are developed in accordance with Santander's risk management framework, model governance standards and regulatory expectations. WHAT YOU’LL BRING Our people are our greatest strength. Every individual contributes unique perspectives that make us stronger as a team and as an organisation. We’re enabling teams to go beyond by valuing who they are and empowering what they bring. The following requirements represent the knowledge, skills, and abilities essential for success in this role. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. Professional Experience: Experience developing models or analytics using Python and SQL on large and complex datasets (Required) Experience developing production ready machine learning or GenAI models (Required) Experience delivering analytical solutions from concept through to production deployment and ongoing monitoring (Required) Experience working with Transaction Monitoring analytics or Financial Crime detection models (Preferred) Experience working within a regulated financial services environment (Preferred) Experience with model governance, model validation or regulatory engagement (Preferred) Education: Bachelor's degree in Data Science, Computer Science, Mathematics, Statistics, Engineering or a related quantitative discipline (Preferred) Languages: English (Required) Hard Skills: Excellent programming skills in Python for data science, machine learning and analytics (Required) Advanced SQL skills for data extraction, transformation and analysis (Required) Solid understanding of statistical modelling, machine learning algorithms and feature engineering (Required) Experience using SAS or R (Preferred) Knowledge of Transaction Monitoring systems, Anti Money Laundering, Counter Terrorist Financing, Sanctions and Anti Bribery & Corruption (Preferred) Experience with Generative AI, graph analytics or network analysis applied to Financial Crime (Preferred) Experience using Git, CI/CD pipelines and software development best practices (Preferred) Soft Skills: Good analytical and problem solving skills with the ability to solve complex business challenges (Required) Excellent communication skills with the ability to explain technical concepts to non technical stakeholders (Required) Ability to manage multiple priorities and deliver high quality outcomes in a fast paced environment (Required) Curiosity and a continuous improvement mindset with a passion for innovation and learning (Required) High attention to detail and commitment to quality and robust governance (Required) WE VALUE YOUR IMPACT At Santander, your contribution matters. We recognise the difference you make every day, and we make sure you feel valued, supported and rewarded in return. Here, recognition goes beyond pay. It’s about the pride you feel in your work, the impact you have on customers and communities, and the opportunities you have to grow and thrive — personally and professionally. Salary Range: £54,318.00 - £81,478.00 per annum (depending on experience) This salary range represents the expected remuneration for the role. Annual salary is based on a standard 35-hour working week. Actual salary offered will depend on skills, experience, qualifications and location. Competitive rewards that reflect the real impact you make and the value you bring. Wellbeing that goes beyond work — we work with a range of wellbeing partners across our 4 pillars of wellbeing (physical, mental, social and financial) to give you access to a suite of apps, discounted gym and fitness access, weekly online classes, flexible healthcare and mental health support. Support for every life stage — from menopause and pregnancy to parenthood and beyond, with enhanced family leave, childcare options and tailored wellbeing support. Time to give back through volunteering opportunities that let you make a difference in the communities we serve. Global growth opportunities to shape your career, learn new skills and explore what’s possible across our international network. Ready to be recognised? It starts with you. LOCAL COMPLIANCE At Santander, we’re proud to be an inclusive organisation that provides equal opportunities for everyone — regardless of age, gender, disability, civil status, race, religion or sexual orientation. We’re committed to creating a recruitment experience that’s accessible, fair and welcoming for all candidates. We want our people to thrive — at work and at home — while delivering the best outcomes for our customers and supporting each other to grow. To make this possible, our roles are site-based with a hybrid working pattern , where colleagues are expected to attend the office at least 12 days per month (pro-rata for part-time roles). When applying, please consider the travel distance, time and cost to your chosen office locations. We welcome applications on the understanding that, should you be offered this role, there may be no relocation package available. Santander will pay the employer mandatory government fees that are required to pay in connection with visa sponsorship. You may be liable for your own personal employee immigration and relocation costs. WHAT TO DO NEXT If this sounds like a role you are interested in, then please apply. For further Guidance please visit Internal Mobility - Candidate Guidance . If you want to know more about the opportunity: Please contact Avnish Popat – [email protected] #LI-DNI