Applied Data Scientist

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.

• Contribute to the design, development, and implementation of statistical models and machine learning algorithms to solve business problems and derive actionable insights from datasets.
• Collaborate with cross‑functional teams to help refine business requirements, ensuring that data‑driven solutions support organizational goals and have measurable impact.
• Assist in the management of data science projects, including data collection, analysis, model development, and deployment, ensuring timely and effective delivery.
• Engage with stakeholders to present findings and insights, communicating technical concepts clearly and effectively to non‑technical audiences.
• Support the team in adhering to best practices in data science and machine learning, contributing to process improvements and knowledge sharing.
• Independently work on assigned data science tasks, solving problems and delivering solutions that align with team goals.
• Collaborate with team members to ensure that proposed solutions comply with internal processes and governance standards, adapting to business needs as required.

• Proficiency in Python, with knowledge of core data science libraries (e.g., Pandas/Polars, Num Py, scikit‑learn).
• Experience in developing and deploying analytics pipelines that are scalable and efficient.
• Solid understanding of machine learning/AI, with hands‑on experience applying both traditional machine learning models and some deep learning techniques.
• Familiarity with version control systems (e.g., Git) and experience working in an environment that follows software development best practices.
• Experience in the end‑to‑end lifecycle of data science projects, from data analysis to model deployment.
• Strong understanding of data analysis, statistics, and mathematical modelling, and how to apply these skills to real‑world problems.
• Comfortable working within UNIX/Linux environments and containerised environments (Docker).
• Strong problem‑solving skills, with the ability to identify and address key challenges in data‑driven solutions.
• Good communication skills, with the ability to explain technical concepts to both technical and non‑technical stakeholders.
• Ability to work independently in isolated environments, using offline resources and tools to analyse data, write Python code, and solve problems in a secure, self‑contained setting.

• Experience with Natural Language Processing (NLP), including techniques like text classification.
• Familiarity with Jira and Agile methodologies for project management and team collaboration.
• Proficiency in SQL.
• Previous experience in regulated environments or industries with governance, compliance, and risk management requirements.

• Bachelor’s degree in computer science, data science, physics, mathematics, or a related field (or equivalent work experience).
• Ability to work both independently and collaboratively in a fast‑paced, dynamic environment, delivering high‑quality results.
• Strong attention to detail and organisational skills, with the ability to handle competing priorities and ambiguity.
• Communication skills that enable you to explain technical concepts clearly to both technical and non‑technical teams.
• Results‑oriented, with a focus on delivering impactful solutions and always seeking opportunities for continuous improvement.