Senior Applied Data Scientist

Senior Applied Data Scientist

SWIFT

Leiden, Netherlands

About the Role

You are applying for an Applied Data Scientist position in the team that has access to one of Swift’s most valuable assets: its data. Your mandate is to find innovative ways of leveraging that data, either to support the strategy of the company or to design new data-driven insight or solutions for our customers. How? By leveraging AI and data science techniques. As an applied data Scientist you will be involved in topics including anomaly detection, predictive modelling, synthetic data generation and exploring the opportunities of generative AI. The financial industry is a fast-moving environment as emerging firms are making inroads into critical financial services. This fast-paced evolution is reshaping financial services providing consumers with new, convenient options while challenging established norms. Swift has a unique position in the global financial ecosystem, meaning you will have the opportunity to design services that have a global impact and high added value for our customers.

The team you will be joining is composed of Applied Data Scientists and Automation experts working in the Ai, Analytics and Automation Tribe.

Responsibilities

• Lead the design, development, and execution of advanced statistical models and machine learning algorithms to solve complex business problems and derive actionable insights from large datasets.
• Collaborate closely with cross-functional teams to define and refine business requirements, ensuring data-driven solutions align with organizational goals and have measurable impact.
• Own the full lifecycle of data science projects, from ideation and data collection through to deployment, ensuring scalable, high-quality solutions.
• Provide technical leadership and mentorship to junior team members, fostering a culture of innovation, continuous learning, and collaboration within the data science community and the AI, Analytics and Automation tribe.
• Proactively engage with stakeholders at various levels, delivering clear, compelling presentations, and effectively communicating complex technical findings to non-technical audiences.
• Champion best practices in data science and machine learning across the organization, promoting knowledge sharing and driving the continuous improvement of methods and processes.
• Ensure that proposed data science solutions comply with internal governance and compliance standards, adapting to evolving business needs while maintaining flexibility and efficiency.

Must haves

• Expert-level proficiency in Python with deep knowledge of its data science ecosystem (e.g., Pandas/Polars, NumPy, scikit-learn, PyTorch).
• Proven experience in designing, building, and deploying robust analytics pipelines that are scalable, efficient, and deliver value.
• Strong background in machine learning/AI, with hands-on experience using advanced models and frameworks across supervised, unsupervised learning, and deep learning.
• Expertise in version control systems (e.g., Git) and experience working with CI/CD pipelines and software development best practices.
• Extensive experience in managing the end-to-end lifecycle of data science projects, ensuring timely delivery, scalability, and alignment with business objectives.
• Advanced proficiency in data analysis, statistics, and mathematical modelling, with the ability to apply these principles to solve real-world business challenges.
• Expertise in working within UNIX/Linux environments and containerized environments (Docker).
• Strong self-starter mindset with a proactive approach to identifying and solving impactful challenges independently.
• Exceptional communication skills, with the ability to clearly explain complex technical concepts to both technical and non-technical stakeholders.
• Ability to work independently in isolated environments, leveraging offline resources and tools to analyze data, write efficient Python code, and solve problems in a secure, self-contained setting.

Nice to have

• Experience with Natural Language Processing (NLP), including classical techniques (e.g., text classification) and more advanced methods (e.g., transformer-based models).
• Familiarity with Jira and Agile methodologies for managing projects and collaborating within cross-functional teams.
• Proficiency in SQL for efficient querying of large datasets.
• Experience working in regulated environments (e.g., financial institutions) with a strong understanding of governance, compliance, and risk management frameworks.

Requirements

• Bachelor's or advanced degree in computer science, data science, physics, mathematics, or a related field (or equivalent work experience).
• Demonstrated ability to work independently and collaboratively in a fast-paced, dynamic environment, consistently delivering high-quality results.
• Strong attention to detail, organizational skills, and the ability to effectively manage competing priorities and ambiguity.
• Excellent interpersonal and communication skills, with the ability to translate complex data science concepts into actionable insights for diverse stakeholders.
• Results-oriented and highly motivated, always striving for continuous improvement and operational excellence.