Jobs

Fair Isaac Corporation is the preeminent provider of creative analytics that unlock value for people, businesses and industries. Our predictive modeling, decision analysis, intelligence management, decision management systems and consulting help businesses make better decisions in the areas of customer targeting and acquisition, customer origination and customer management. Most leading banks and credit card issuers rely on Fair Isaac solutions, as do insurers, retailers, telecommunications providers, healthcare organizations and government agencies.

Join us as an Analytic Science Lead Consultant in our San Rafael, CA office.

Responsible for:

• Managing the analytic components of projects, including project schedules, workflow, and direction to the analytic team;
• Analytic leadership in thinking through key project decisions;
• Ensuring the client's business objectives are incorporated into the analytic plan;
• Communicating key analytic findings to client, and responsible for addressing client's analytic concerns under Delivery Manager guidance;
• Educating clients on Enterprise Decision Management technology, e.g., "demo-ing" the science;
• The trade-off between analytic quality and speed/cost of production;
• Conceiving, planning and conducting investigations and experiments in uncharted areas;
• Inventing new methodologies or approaches to problems and translates them into improvements in Fair Isaac products or tools;
• Developing quality control standards; designing quality control procedures;
• Serving as mentor and fostering the career development of entry- and intermediate-level professional staff members.

• Minimum 4 years experience with an analytic solutions or consulting company, with at least 2 in a client-facing project management capacity;
• Master's or bachelor's degree in a technical field; experience in applying statistics, mathematics and operations research techniques to the solution of data-driven business problems;
• Knowledge and experience in applying data to solve business problems through quantitative analysis;
• Experience with predictive modeling, optimization and similar data-driven business solutions;
• Knowledge of the principles and practices of project management, and work scheduling.

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