Wagestream is on a mission to bring better financial wellbeing to frontline workers.We partner with some of the worlds most famous employers, like Burger King, Next, Asda, Co-op, Bupa and Green King to give their teams access to the UKs leading Financial Wellbeing App through fairer financial services - all built around flexible pay. Over three million people can now choose how often theyre paid, track their shifts and earnings, start saving, use budgeting tools, get free financial coaching, and access fairer financial products. All in one financial wellbeing super-app.Wagestream is unique: VC-backed and growing at scale-up pace, but with a social conscience. Some of the world's leading financial charities and impact funds were our founding investors, and we operate on a social charter - which means every product we build has to improve financial health and reduce the $5.6bn premium lower-income earners pay for financial services each year.Youd be joining a team of over 200 passionate, ambitious people, across Europe and the USA, building a category-leading fintech product and all united by that same mission.The Opportunity:The credit team currently consists of credit risk, compliance and financial crime experts, software engineers, product designers, data analysts and data scientists who all work collaboratively to meet the ambitious growth targets for our credit card and loan products.The focus of data scientists in the Wagestream credit team is to design, build and deploy machine learning models that make predictions about customer credit risk and churn, as well as support marketing decisions, financial crime and product offerings.We work closely with the wider Wagestream data team and analytics engineers to cleanse, organise and engineer Wagestreams large proprietary data set and data from external sources to best support robust decision making. Our insights feed directly to the credit management team, supporting product growth, reducing credit losses and improving product features and performance.The key technologies we use are Python (Pandas, Scikit-learn), AWS, Snowflake, DBT and PostgreSQL.What will you be doing? Design, train, evaluate and support the deployment of machine learning models to predict customer credit and churn risk, as well as other models to support product, marketing and financial crime. Work directly with subject matter experts to understand business requirements and ensure that machine learning models serve the business problem at hand. Monitor and maintain model performance, provide insights into potential problems and performance degradation. Provide fast, high-quality outputs to ad-hoc requests for data / visualisations / analytics where required. Be able to flex between different tools depending on the goal (e.g. python vs SQL, modelling vs deep dive analytics, training models vs supporting their deployment to production). Be proactive about suggesting new processes and technologies to optimise underlying data processes, or enhance the product and user experience.What skills / experience might you have?Must-haves: (But if youre close we'd still love to talk to you!) Youre excited about our mission and product, and are comfortable working in a fast-paced environment. Experience building feature engineering pipelines and machine learning models, from business problem to model productionisation - ideally 5+ years (2+ years in industry). Experience with model interpretation and audit, ability to effectively communicate model performance and how models are taking decisions. Experience leading or having a major role in machine learning projects. Python, particularly pandas (or equivalent, such as polars / dask) and machine learning libraries, inc. scikit-learn / tensorflow / keras - ideally 5+ years. Experience using SQL to query large datasets. Experience of software development practices, such as version control, testing, documenting and continuous integration. Good written and verbal communication skills. Curiosity about the world of credit and keen to use data and scientific principles to support our products. You take responsibility for the quality of your work.Nice-to-have: Experience in financial services and credit. Experience working in a regulated environment. Experience writing production code. Experience with DBT and snowflake. Used to working in an agile way. Awareness of data governance issues and GDPR.Working policy: HybridSalary: Dependant on skills and experience. Salaries range from 80,000 - 110,000 + 20% bonus % stocks.Benefits: 25 Days Annual Leave in addition to public holidays (up to 5 day rollover), as well as flexible time off allowances for any ad-hoc childcare/family/caring needs. 10 days Annual Leave Buy-Back scheme - for if youd like some additional time off. 12 weeks paid Maternity Leave and 4 weeks paid Paternity Leave for employees with over 12 months service. 250 home office allowance to make WFH comfortable. Brand new equipment - from the latest App
Location: New York, NY
Posted: Oct. 21, 2024, 9:38 p.m.
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