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Data Scientist, London, Permanent

Help us to develop Pay.UK's data science, machine learning, AI and predictive analytics capability by building robust predictive and forecasting models. This is a great opportunity to get stuck in and really take data analysis to the next level. You will also..

• Provide quantitative analysis in support of Pay.UK strategy and development initiatives.
• Use advanced data science techniques to identify trends, patterns and discrepancies in data and determine and direct gathering of additional data needed to support insights.
• Manage the development of strategic insight and research to support the payment ecosystem end to end and manage the distribution of published material, leveraging commercial opportunities as appropriate.
• Maintain understanding of the end-to-end payments landscape combining insights and data internally across Pay.UK, stakeholder engagement and horizon scanning of the industry leveraging from internal and external market analytics capabilities.
• Facilitate the transparency of the ecosystem to the supplier market, and provide insight that will support the mitigation of fraud.
• Apply data strategy including maintaining associated Data Policies that enable Pay.UK strategy and ecosystem needs (GDPR), and drive commercial opportunities that may arise from data exploitation.
• Use data modelling techniques to analyse market scenarios and communicate recommendations to business

Key Accountabilities

• Work with Big Data technologies to shape the development of analytical insight delivery.
• Use statistical data approaches to uncover patterns in data from which predictive models can be developed.
• Identify valuable data sources and automate collection processes.

Apply statistical techniques on large datasets to:

• Measure results and identify key underlying trends
• Build hypothesis and test significance
• Identify predictive indicators and causal impact and attribution
• Build predictive models to identify the likelihood of fraud
• Analyse geo-demographic and socio-economic profiling data along with transactional and behavioural data to produce critical insights and business deliverables
• Actively review market movements and industry research for insight on market dynamics, evolution of infrastructure and changes in payments sector
• Present findings to key stakeholders, and input to conversations on strategies to drive performance across the business
• Collaborate with colleagues and stakeholders to build data and analytical products that enable others to work with reporting effectively and making informed decisions that solve real business problems
• Maintain best-practice standards relating to data science techniques, coding and analytics
• Apply data science methods including regression (linear, logistic, multiple, multivariate), decision trees, segmentation and experimentation to drive opportunities and business decisions.
• Manage all aspects of data science project lifecycle and deliver within planned timelines
• Maintain industry recognised best practice including ML algorithm selection, propensity modelling, documentation, quality assurance, data governance and data privacy policies

Qualification, Skills & Experience

• Degree in a STEM subject or equivalent experience
• Professional experience applying data science and statistical methods with real data problems
• Expert knowledge in coding using Python or R
• Significant exposure in a Data Science and business intelligence technology stack (anaconda, Jupyter NB, databricks, R studio)
• Good knowledge and experience working with visualisation tools. e.g. Power BI, Tableau, etc
• Good knowledge and experience using SLQ and working with databases, including design, modelling and architecture e.g. SSMS, SSAS, SSRS and SSIS
• Good understanding of the Financial Services / Retail Payments market
• Strong experience with building predictive models and applying advance statistical forecasting
• Use statistical data approaches to uncover patterns in data from which predictive models can be developed.
• Experience with Cloud environments beneficial.
• Experience using a variety of data science tools, and selecting the right approach for the type of data or problem
• Professional qualification preferred but not essential
• Good understanding of Econometrics and how macroeconomic events can impact population, economy and ultimately the payments sector
• Strong skills in building predictive models to identify business opportunities and support informed decision making
• Experience using a variety of data science tools, and selecting the right approach for the type of data or problem
• Excellent stakeholder management skills coupled with the ability to quickly understand stakeholder needs and define and ensure the implementation of corresponding actions
• Ability to find innovative solutions to solve business problems and demonstrate intellectual curiosity and always strive to learn
• Build dashboards to visualise modelling outputs using visualisation tools such as Power BI and other tools suitable for disseminating agent-based simulation outputs