We are looking for a seasoned leader to head Product Development for Guardinex. The ideal candidate will have experience in Credit and Fraud analytic solutions with deep experience enabling insights from disparate data sources to build risk scoring products to solve critical unsolved problems.

What you’ll need

• Strong familiarity with the domain: Third and First party fraud, including metrics of success, domain driven analysis methods, visualization techniques
• Strong background in features correlated with fraud and default , very familiar with customer segmentation, risk modeling, (and other domain related concepts)
• Familiarity with datasets of relevance: application datasets, credit and transaction datasets, external datasets such as – Email, IP, Phone etc )
• Ability to translate customer requirements to modeling problems, success criteria and recommend high level plan.
• Ability to guide and lead data science teams in exploratory data analysis, analysis of model results, and domain based input on feature engineering including dealing with noise, errors and missing data
• Ability to validate and signoff on model before product release. Ability to provide requirements for tools Subject Matter Experts should use ( validation tool, analytics reports etc )
• Ability to carry out basic analysis using tools of choice (sql, no code analysis tools, excel etc )
• Familiarity with concepts in machine learning and statistics : precision, recall, sensitivity, specificity , concept drift, feature importance, xgboost, etc is desired
• Attention to detail is a must and rigor in analysis is strongly desired
• Comfortable working in cloud based systems

What you’ll do

• As head of Product, you will work closely with the Data Science, Leadership and Technology teams to develop new risk scoring products targeting various types of Fraud and Risk
• Leverage data to understand the product performance and to identify improvement opportunities.
• Design product experiments and interpret the results to draw detailed and impactful conclusions
• Provide recommendations to assist quick product ideation and feature launch decisions and build intelligent data-driven products to provide the best user experience
• Generate ideas for exploratory analysis to shape future projects and provide recommendations for actions

Basic Qualifications:

• Experience in Fraud/Fraud Detection/AML/Risk Analytics is preferable
• Experience solving ambiguous business problems at scale
• Bachelors degree or higher in, Statistics, Economics, Machine Learning, Operations Research, Computer Science or other quantitative fields.
• 2+ years of hands-on experience in developing risk scoring models
• Knowledge of underlying mathematical foundations of statistics, machine learning, optimization, economics, experimental design and analytics

Preferred Qualifications:

• Experience with exploratory data analysis, statistical analysis, testing, and model development
• Proficiency in languages and tools like SQL, Python, AWS to work efficiently at scale with large data sets
• Experience with Feature Engineering, experimental design and analysis and Causal inference techniques
• Experience building production-level machine learning and deep learning models
• Experience with productionizing ML algorithms for real-time systems