Data Scientist

• Develop Statistical, Machine Learning and Deep Learning solutions to tackle fraud and risk
• Manage the end-to-end model lifecycle including real-time model serving
• Build and use data pipelines to extract and communicate meaningful insights from large datasets
• Conduct data-backed experiments and analyses to and fine-tune model performance across a variety fraud MOs and enterprise use cases
• Contribute to developing production-grade software applications backed by statistics and ML

What you bring

• BS, MS or Ph.D in Machine Learning, Computer Science, Mathematics, Statistics or other quantitative disciplines. Experience in Machine Learning Engineering or Data Science roles.
• Proficiency in Python and scaling out to the cloud (AWS/Databricks/Azure)
• Strong familiarity with SQL, data modelling (preferably DBT), distributed analytic processing technologies (Athena, Big Query) and visualization platforms (Superset, Looker, Tableau)
• Proven expertise in writing production code and deploying DS/ML applications, preferably in fintech fraud, risk, payments or compliance
• Prior experience in building ML observability to uphold the performance and reliability of ML models in production
• Skilled in Exploratory Data Analysis to investigate ad-hoc questions and explain anomalous data
• Some familiarity writing backend code and experience with Golang and efficient containerization using Kubernetes, Docker etc., is a plus
• Advanced expertise in software engineering principles
• Creativity in building data science solutions in the absence of feedback or labelled data
• Ability to thrive in an unstructured environment, working autonomously on a strong team to find opportunity and deliver business impact.