Risk Decision Scientist

Paysafe Limited (“Paysafe”) (NYSE: PSFE) (PSFE.WS) is a leading payments platform with an extensive track record of serving merchants and consumers in the global entertainment sectors. Its core purpose is to enable businesses and consumers to connect and transact seamlessly through industry-leading capabilities in payment processing, digital wallet, and online cash solutions. With over 20 years of online payment experience, an annualized transactional volume of $140 billion in 2023, and approximately 3,200 employees located in 12+ countries, Paysafe connects businesses and consumers across 260 payment types in over 40 currencies around the world.

Delivered through an integrated platform, Paysafe solutions are geared toward mobile-initiated transactions, real-time analytics and the convergence between brick-and-mortar and online payments. Further information is available

It starts here. Have a global impact on the world of payments.

This role is part of the Risk Systems & Strategy team within the broader organization led by the Chief Risk Officer (CRO). You will focus on the analysis of merchant risk activities for our merchant products, including, ecommerce, point of sale and eCash solutions, playing a key role in understanding merchant behaviour and influencing the strategic direction of the company. The key stakeholders for this role are the Sr Director of Risk Systems, VP of Risk Transformation, SVP of Financial Risk, and their teams.

You’ll identify new risk management & loss mitigation opportunities, highlight how to optimize existing strategies, develop new ones, minimise false positives, and make clear actionable recommendations that drive decision making.

You’ll become an expert on the merchant risk & fraud risk data, while also working closely with and relying on the support of the data Engineering & data Modelling team. In addition to your work on Merchant risk, you’ll contribute to high-impact strategic projects that offer business-wide exposure and interaction with C-Level stakeholders, helping you to broaden your knowledge and build your personal brand.

You will have the space to take ownership of projects as well as contribute to the strategy of the Merchant Risk domain. You are comfortable working with projects and tasks where not everything is defined, come up with creative solutions, and involve your manager where support is needed.

• Being
  open
  and honest.
• Keeping
  focused
  .
• Operating with
  Courage
  .
• Pioneering the future.

Our values and culture are driven by Equality, Development, Social Responsibility and Wellbeing. If you want to find out more about life at Paysafe, check out our careers pagehere .

We follow a hybrid working model, spending an average of
three days per week
at our office location.

The office is located in Gresham Street next to St Paul’s cathedral with easy access and transport links via St Paul’s, Bank, Cannon Street, City Thameslink, Liverpool Street, Farringdon, Mansion House.

• Undertake deepdive analysis into merchant behaviour to identify patterns, trends, and root causes. Make clear recommendations on how to capitalise on opportunities, feeding into and driving the business strategy with insights.
• Apply structured problem solving with the ability to logically break ambiguous problems into constituent parts.
• Analyse the success of risk strategies using advanced approaches such as statistical hypothesis testing, causal inference, A/B testing, and uplift testing.
• Create self-serve data products and dashboards about the merchant risk programme while ensuring data reliability and accuracy.
• Contribute to the merchant risk analytics space by helping to define the right data to be collected, collaborating with the data engineering & modelling team to ensure data pipelines & models are working smoothly, and assisting in creating documentation.
• Communicate effectively to a mixture of technical and non-technical stakeholders of varying seniority. Tell a story with the data and ensure key points and recommendations are understood.
• Depending on your skillset, either incorporate machine learning techniques (e.g. predictive loss…