Model Validation Data Scientist

November 5

Apply Now

Receive Emails with Similar Jobs

WEX

WebsiteLinkedInAll Job Openings

Fleet payments • Heathcare payments • Travel payments • Virtual payments • Corporate payment solutions

5001 - 10000

💰 $310M Post-IPO Debt on 2020-06

Description

• Designs, develops and programs methods, processes, and systems to consolidate and analyze unstructured, diverse “big data” sources to generate actionable insights and solutions for client services and product enhancement • Interacts with product and service teams to identify questions and issues for data analysis and experiments • Develops and codes software programs, algorithms and automated processes to cleanse, integrate and evaluate large datasets from multiple disparate sources • Identifies meaningful insights from large data and metadata sources; interprets and communicates insights and findings from analysis and experiments to product, service, and business managers • Apply data science domain knowledge to perform technical independent validation of machine learning and statistical models • Develop and enrich risk domain expertise by engaging with experts in Credit Risk, Fraud Risk, Financial Forecasting, and Operations to understand model design requirements • Assess code and implementation design for model development and deployment • Design creative testing and automated scripts to assess model robustness on large volumes of data and a variety of conditions • Keep abreast with emerging best practices in risk modeling, machine learning, product development, and strategy to apply and improve processes • Synthesize findings into actionable insights and articulate them both in narrative documentation and verbal presentations to senior leadership • Proactively identify and communicate challenges, opportunities, and risks associated with models and projects

Requirements

• Master’s or Ph.D. degree in a quantitative field such as Data Science, Mathematics, Computer Science, Statistics, or other technical field • 2+ years of professional or research experience as a data scientist, model developer, model validator, statistician, or applied scientist • Understanding of inner-workings of statistical and machine learning algorithms and their strengths and weaknesses • Excellent analytical problem-solving and critical thinking skills with attention to detail • Proficiency with SQL to extract and transform large datasets • Proficiency of scripting languages such as Python or R and experience with common data science libraries such as scikit-learn, lightgbm, pandas, numpy etc • Strong written and oral communication skills with an ability to relate complex analytics findings to business outcomes • Solutions oriented and proactive to solve problems collaboratively both in a team of technical and non-technical colleagues and independently in a self-starting manner

Benefits

• health, dental and vision insurances • retirement savings plan • paid time off • health savings account • flexible spending accounts • life insurance • disability insurance • tuition reimbursement • more