Job Details

*"You will be an integral**part of a team of scientists working closely with external clients to**build efficient predictive models,**analy**z**e**and validate production model behavio**r and ensure optimal model performance.**There will be opportunities to**support the internal technology team in development of models and**get involved in**sales activities related to the evaluation of models for different client environments."* – Sr. Manager Analytic Science Manageprojects under time and resource constraints, work with other teams within FICO (software, IT, product management, and product support) provide high quality support to enable integration and deployment of analytics software and solutions. **What We're****Seeking** Some related industry experience in predictive modeling and data mining or PhD with no related experience. Experience with Perl/Python, C, C++, or Java and familiarity with basic software design principles and coding standards and best practices. Experience analyzing large datasets and applying data-cleaning techniques along with performing statistical analyses leading to the understanding of the structure of datasets.Prefer knowledge of some of the following: Bayesian networks, PCA, independent component analysis, linear and logistic regressions, inference, estimation, experimental design, neural networks, SVM. * An inclusive culture strongly reflecting our core values: Act Like an Owner, Delight Our Customers and Earn the Respect of Others.* The opportunity to make an impact and develop professionally by leveraging your unique strengths and participating in valuable learning experiences.* Highly competitive compensation, benefits and rewards programs that encourage you to bring your best every day and be recognized for doing so.* An engaging, people-first work environment offering work/life balance, employee resource groups, and social events to promote interaction and camaraderie.* The targeted base pay range for this role is: $98,000 to $154,000 with this range reflecting differences in candidate knowledge, skills and experience.#J-18808-Ljbffr