Analytic Science Lead Scientist

Responsibilities

• Work with large amounts of real-world data, ensuring quality through collection, normalization, and transformation.
• Research and select appropriate statistical methods and computational algorithms.
• Build and oversee teams developing high-end analytic models using statistical modeling techniques.
• Manage projects under time constraints and collaborate with internal teams to enable integration and deployment of analytics solutions.
• Perform production data validations and analyze models in production to assist with go-lives.
• Support client meetings by investigating model behaviors and providing data mining analysis.

Requirements

• MS or PhD in Computer Science, Engineering, Physics, Statistics, Mathematics, Operations Research, or a related natural science field.
• Significant hands-on experience in predictive modeling and data mining.
• Experience analyzing large datasets and applying data-cleaning and statistical analysis techniques.
• Proven ability to lead cross-functional teams on medium to large-scale projects.
• Strong scripting experience using Perl, Bash, or similar.
• Proficiency in at least two of the following: Java, Python, C++, or C.
• Demonstrable experience with at least three of the following: neural networks, logistic regression, non-linear regression, random forests, decision trees, support vector machines, or linear/non-linear optimization.

Benefits

• Highly competitive compensation and rewards programs.
• An inclusive, people-first work environment with a focus on work/life balance.
• Opportunities for professional development and continuous learning.
• Access to employee resource groups and social events.

About the Company

FICO is a leading global analytics software company that helps businesses in over 100 countries make better decisions. From credit scoring to fraud detection, FICO's solutions are used by some of the world's largest financial institutions and retailers to drive success through artificial intelligence, machine learning, and optimization.