Data Scientist, Creditspring

• We are seeking an experienced and detail-oriented Data Scientist to join our Underwriting - Credit risk data science team in either our London office or Bengaluru office
• This is a mid-level individual contributor role, ideal for someone who thrives on solving complex problems, driving innovation, and applying advanced analytics and machine learning to real-world business challenges
• The role is suited to a well-rounded candidate, with strong project management skills and experience of acting upon produced insights
• Quantitative degree with 3-5 years of prior experience in in credit risk analytics, preferably within an SME or retail lending environment
• Experience developing and deploying machine learning models in a local and cloud environment. Familiary with regression and gradient boosting techniques, model development best practices for model tuning, feature engineering, validation and explainability
• Strong command of statistical inference and supervised machine learning stack (scikit-learn, pandas, numpy, jupyter). Solid knowledge of Python for data extraction, transformation and analysis
• SQL proficiency in manipulating, merging, and cleaning or checking data from multiple sources including internal data and external feeds
• Commercial awareness with strong communication skills and the ability to influence stakeholders via analytics delivery
• Don’t meet all the listed requirements? Research shows that women and people of underrepresented groups often don't apply for jobs unless they're 100% qualified. As an equal opportunities employer, we know that diversity is a key part of our teams' successes - so if your experience doesn’t fit perfectly but this role excites you, we’d love for you to apply

Desirable

• You will be instrumental in shaping company’s credit risk models, monitoring performance, optimising product offerings and contributing to the development of production solutions that directly impact our members’ financial wellbeing
• Sitting at the intersection of Data, Engineering, Operations, Product and Marketing, the role is critical to support further platform growth and credit product innovation
• It offers an opportunity to develop and deepen data science, business and system analytics skills
• This is a full stack data science and analytics role – where a lot of time and effort will be spent on data extraction, wrangling, mining and feature engineering. The team has a strong focus on Consumer Duty/regulatory compliance and delivering measurable impact on the commercial objectives of the company
• Ideate and build robust machine learning models for credit risk assessment and adjacent use cases – collection initiatives, identity resolution, affordability assessment, macro-resilience and decision explainability
• Supervise model deployment, by testing, monitoring performance and ensuring timely redevelopment and recalibrations. Identifying data and model drift
• Contribute to the development and optimization of our data pipelines, tooling, and infrastructure
• Coordinating change processes related to credit lifecycle - from idea generation, proposing solution to project management, deployment and monitoring
• Become an expert on the external API feeds used in decisioning – credit reference agencies, open banking data providers and alt-datasources
• Partnering with other teams to assess feasibility and support various growth initiatives, designing and implementing acquisition, product and lending strategies