• Bachelor’s degree or above in Math, Economics, Bioinformatics, Statistics, Engineering, Computer Science, or other quantitative fields
• For candidates with Bachelors and Master’s, minimum of 7 years of industry experience as a Data Scientist, Research Scientist, or equivalent. For candidates with Doctor Degree, minimum of 3 years of industry experience
• Strong familiarity with the mathematical fundamentals of advanced statistics, optimization, and/or economics, as well as methods for experimental design and causal inference
• Experience working with large datasets in Python, Strong knowledge of SQL (preferably Redshift, Snowflake, BigQuery)
• Strong python experience (numpy, pandas, sklearn, pytorch etc.) across exploratory data analysis, predictive modeling, and applications of ML techniques
• Strong communication: the ability to bridge technical methodology to meaningful data narratives to drive company decisions and strategy
• Track record of shipping high quality data products in production and at scale
• Ability to thrive in a fast-paced, constantly improving, start-up environment that focuses on solving problems with iterative technical solutions

Desirable

• We’re looking for someone to help lead the future of credit applied science at Ramp
• The Applied Science team at Ramp creates value by building the models powering decision-making
• You will need to have a head for strategy & cross-functional collaboration, since you will partner closely with business & product stakeholders to prioritize, execute, and drive results
• You will also partner closely with the rest of the data team and the engineering team to design, implement, and maintain data science models in production
• Applied scientists at Ramp focuses on solving quantitative problems across credit, fraud, growth, and our core product by applying the right mix of causal inference, structural modeling, and optimization
• Full stack data science development: from upstream data modeling and cleaning, to research and prototyping, to deploying and monitoring models in production
• Contribute to the company roadmap by working closely with stakeholders throughout the lifecycle of prioritization: from complex and nebulous business context, to well-defined objectives, to a roadmap of scoped opportunities for leveraging data science to drive business results
• Leverage a combination of causal inference, structural modeling and optimization to build key models to solve core business problems
• Formulate backtesting frameworks to empirically validate model performance
• Generate and communicate data-driven insights to influence decision making across Ramp