Senior Data Scientist

Job Description

At Visa, we are passionate about making a difference. We lead the way in disrupting fraud from multiple vectors. In this role you will be joining an exciting, innovative business new to the Visa family.

At Featurespace, we strive to be the world’s best software company at protecting our clients and their customers from fraud attacks. We do that with personality, heart and professionalism, cultivating an innovative, fun and positive team atmosphere where everybody can contribute to solving our clients’ problems in new, innovative ways. We are always seeking to be the best at what we do and make our customers smile.

The Opportunity

In your role as Senior Data Scientist, you will help us achieve our goals and deliver success on behalf of our customers by:

• Developing and maintaining the end-to-end analytic products that exist within our Solutions; including data schemas, advanced statistical models, reporting configuration, and documentation
• Collaborating across the organisation with commercial, product, delivery, and engineering team to uncover opportunities to work on, share insights, and develop products

Responsibilities

We hire people with a willingness to adapt to a variable role, so along with the key responsibilities below, we ask for ownership of any other duties as required:

• Interacting with users (both internal and external) to understand their problems and sharing this insight with the rest of the team
• Collaborating with Product Managers & other members of the team to align on the highest value items to work on
• Coordinating work across multiple teams & when needed, taking on additional “tech lead” responsibilities for driving initiatives to completion
• Identifying risks and testing assumptions before development
• End-to-end processing and modelling of large customer data sets
• Leading the deployment and maintenance of statistical models and algorithms
• Testing analytical models and their integration within the Featurespace platforms
• Ensuring high quality documentation exists alongside analytics products (reports, presentations, visualizations)
• Measuring, documenting, and improving outcomes associated with analytic products
• Supporting the delivery teams delivering analytic products
• Enabling both technical and non-technical colleagues by effectively communicating insights learnt during discovery and data analysis
• Evangelizing on the benefits of the analytic products within Featurespace
• Recruiting for Data Scientists within the team
• Improving team processes and providing input to future team strategy
• Mentoring more junior members of the team as well as managing and prioritising their workload to ensure high-quality output
• Developing a solid understanding of the fraud and financial crime industries

This is a hybrid position. Expectation of days in the office will be confirmed by your Hiring Manager.

Qualifications

Must haves

• Good degree in a scientific or numerate discipline, e.g. Computer Science, Physics, Mathematics, Engineering or equivalent work experience
• Experience leading the deployment of machine learning models into high throughput, real-time prediction contexts
• Commercial experience implementing statistical models and analytics algorithms in software
• Experience using Python, Java, or another major programming language for data analysis, machine learning or algorithm development
• Technical and analytical skills with the ability to pick up new technologies and concepts quickly
• Problem solving skills (especially in data-centric applications)
• Strong, clear, concise written and verbal communication skills
• Ability to manage and prioritise personal workload
• Constructive participation in system architecture/design discussions from an analytical and business impact perspective
• Practical experience of the handling and mining of large, diverse, data sets
• Experience of collaborating with multiple stakeholders on technical projects

Great to haves

• Ph.D. or other postgraduate level qualification with good mathematical background and knowledge of statistics
• Experience working in a Linux command line environment
• Experience using SQL to analyse data
• Experience with version control software and workflows (e.g. git)
• Experience mentoring junior members of a team
• Experience with the product development lifecycle (discovery, prototyping, implementation, iteration, sunsetting etc.)
• Subject matter expertise in the banking and payments industry