Job description

The Opportunity

As a Lead Data Scientist, you will play a crucial role in ensuring success for our customers by leading a team delivering the analytical capabilities that are at the heart of our mission to make the world a safer place to transact.

At Featurespace, we produce state-of-the-art machine learning models and decision systems protecting banks, payment providers, and hundreds of millions of cardholders and accountholders from fraud and financial crime every day.

We do this by providing our customers with an analytics execution platform, ARIC, which responds in real-time to stop transaction and payment fraud in its tracks. The platform also enables our customers’ fraud and risk operations teams to review high-risk and suspicious activities identified by their analytics, orchestrate communication with their account and cardholders, and track closely the effectiveness of those analytics and operations.

Our Analytics team are based across both our Cambridge and London offices – this is a hybrid role so you will ideally be comfortable coming into one or other of the offices around twice a week. If you’re interested in the role but require more flexibility, please speak to us!

Day to Day

It can be hard to know accurately what you’ll be doing day to day from a job description. That’s why we’re telling you exactly what your team does! There are three key tasks for the Analytics team:

  1. System integration: No analytics execution platform is better than its data integration or customer specific configuration! Our Analytics team work closely with the customer during the system integration to ensure high-quality data is integrated and that the ARIC system is correctly configured to meet the fraud detection demands on the customer.
  2. Analytics and risk strategy consultancy: No analytics execution platform supplied by high-quality, clean data is better than the specific analytics that it runs! Our team work extremely closely with customer fraud and risk strategy teams to ensure that the analytics that our systems run produce industry-leading fraud detection outcomes. We do this by supporting and teaching our customers to use our system effectively but also lending our experience from across the industry and querying data sent to the platform to further optimise the analytics.
  3. Data science: Featurespace’s machine learning models are one of the most attractive propositions to our customers! The final piece in the triad of team activities is the configuration of machine learning models from the rich datasets our customers possess.

You will lead a team working across these three tasks, helping deliver success on behalf of our customers by:

  • Line managing a team in Analytics, acting as both a leader able to spell out a strategic vision and a mentor who grows team members to realise that vision
  • Managing complex deliveries of real-time machine learning models and rules-based systems in a way that builds good relationships with key customer stakeholders
  • Providing consulting expertise to customers that addresses their challenges and demonstrates the value of what we offer
  • Dealing with large data sets and noisy information on complex projects and translating business requirements into well-defined and actionable tasks
  • Managing client teams, and helping to accelerate critical work, including by identifying risks and empowering team members to unblock work before challenges arise
  • Improving internal processes, both operational and technical, in a way that enables scalable work tracking and continual improvement in all responsibility areas, including recruitment, resourcing, and training
  • Be a senior point of knowledge and escalation of analytics related technical questions

About you

Must haves:

  • Good degree in a scientific or numerate discipline, e.g., Computer Science, Physics, Mathematics, Engineering, or equivalent experience
  • Excellent client facing skills, ability to communicate complex analytical concepts to a variety of audiences, especially in a data science context, including the application of practical machine learning algorithms to real-world data
  • Depth of experience in stakeholder management and managing customer expectations and common challenges
  • Ability to manage and prioritise changeable workload ensuring internal and external deadlines are met
  • Experience working with customers to gather complex sets of requirements including analytical system design, data integration design, and model design
  • Knowledge of Python and familiarity with SQL
  • Knowledge of fundamental machine learning concepts (feature engineering, algorithms, model evaluation, model bias, etc.)

Great to haves:

  • Experience in deploying statistical models and analytical algorithms in industry
  • Experience with software engineering, version control, and the Unix command line
  • Practical experience of the handling and mining of large, diverse, data sets
  • Industry experience in financial services, fraud, and fraud strategy
  • Basic knowledge of event-driven systems and distributed computing for stateful systems
  • Experience managing and developing high performing individuals
  • Experience working with model governance bodies or awareness of the issues facing governance of machine learning models in production
  • Experience in requirements management, business analysis or consulting environment

Equal Opportunities

Here at Featurespace we are committed to being a place of equality, inclusion and respect to provide a safe environment for you to bring your authentic self to work. We know that we gain as much strength from our differences as we do our similarities. We value diversity and are dedicated to listening and learning from each other to build and maintain a positive and productive culture. We appreciate this will be an ever-evolving focus for the business to ensure everyone feels supported and has a sense of belonging.

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