FICO is looking for talented individuals to join their Analytics and Scores Development team to work on programming, data exploration, dataset building, and supporting the analytic software development lifecycle to solve challenging business problems in predictive analytics.
Requirements
- Proven programming skills in Python, Java/Groovy, Perl, and shell scripting.
- Demonstrated experience translating business problems into executable code.
- Experience analyze large datasets and apply data-cleaning techniques.
- Synthesize and manipulate data using big data technologies.
- Ability to use statistical software and data manipulation tools for purposes of data quality evaluation.
- Knowledge of Windows, UNIX, Red Hat Linux operating systems.
- Familiarity working with Amazon Web Services (AWS) - Sagemaker, S3, Athena.
Responsibilities
- Use internal technologies in the development, maintenance and improvement of tools and processes to help solve challenging business problems in predictive analytics.
- Support our existing code base and the overall analytic SDLC.
- Demonstrate self-initiative and innovation by writing new code to continuously evaluate and improve existing code base.
- Build, audit, and analyze large datasets.
- Apply data transformation techniques.
- Work on data engineering and ETL tasks supporting the Data Scientists.
- Work closely with the Data Scientists and other Data Engineers in constructing the best methodologies in generating new tools, code and datasets based on project requirements.
Other
- Highly detailed and demonstrates the ability to execute a given process with meticulous precision.
- Demonstrates the ability to work through complex issues effectively given solution, focusing on the end goal.
- Able to identify errors and audit results.
- 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.
