Requirements:

• Lead the development and deployment of machine learning models and data science solutions to improve Receive product performance across different Wise markets

• Analyse large volumes of customer, transaction and product data to identify trends, patterns, risks and opportunities

• Design and implement experiments to evaluate the effectiveness of product changes, decisioning systems and customer experience improvements

• Build scalable modelling approaches that support better prioritisation, personalisation, risk management and operational decision-making

• Collaborate with analysts, product managers, engineers, operations and risk teams to translate business requirements into actionable data science solutions

• Develop robust data pipelines, algorithms and tools to support production-grade modelling and decision-making

• Stay informed about the latest advancements in data science, machine learning, and payment fraud prevention techniques to ensure state-of-the-art capabilities in the Spend domain

• Proven track record of deploying models from scratch, including data preprocessing, feature engineering, model selection, evaluation, and monitoring

• Solid knowledge of Python, and ability to make and justify design decisions in your code. You know how to use Git to collaborate with others (e.g. opening Pull Requests on GitHub) and are able to review code. Ability to read through code, especially Java. Demonstrable experience collaborating with engineering on services

• Experience working with large datasets and data processing technologies (e.g., Hadoop, Spark, SQL)

• Familiarity with anomaly detection, supervised and unsupervised learning methods, and real-time data analysis

• Experience with statistical analysis and good presentation skills to drive insight into action;

• A strong product mindset with the ability to work independently in a cross-functional and cross-team environment

• Good communication skills and ability to get the point across to non-technical individuals;

• Strong problem solving skills with the ability to help refine problem statements and figure out how to solve them

Nice to Haves:

• Experience with MLOps tools: Airflow, MLflow, AWS SageMaker, AWS S3, AWS EMR, CI/CD

• Prior experience in the fraud domain and a strong understanding of fraud detection techniques

• Experience designing and deploying LLM-based solutions in production

What You'll Be Doing:

• Lead the development and deployment of machine learning models and data science solutions

• Analyze large volumes of customer, transaction, and product data

• Design and implement experiments to evaluate product changes

• Build scalable modelling approaches for better prioritization and risk management

• Collaborate with cross-functional teams to translate business requirements into data science solutions

• Develop data pipelines, algorithms, and tools for production-grade modeling

• Stay informed about advancements in data science and machine learning

Perks and Benefits:

• Opportunity to work behind the scenes of company transactions

• Direct impact on Wise's mission and millions of customers

• Diverse and inclusive work environment

• Passionate about learning new things and keen to join a mission-driven team