Fair Isaac Corporation

Lead Analytic Scientist

Fair Isaac Corporation • $122K — $192K *
San Diego, CA 92154In-Person
Information Technology
5 - 7 years of experience
Reposted Today
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Job Overview by Ladders

Qualifications

  • MS or PhD in a quantitative field (computer science, engineering, statistics, etc.) with relevant hands-on experience.
  • Experience with large data sets and data-cleaning techniques.
  • Proven leadership skills in managing cross-functional teams and client interactions.
  • Strong scripting skills (Perl, Bash, etc.).
  • Proficiency in at least two programming languages (Java, Python, C++, C).
  • Experience with various predictive modeling techniques including neural networks and regression methodologies.
  • Familiarity with real-time payment data handling is a plus.

Responsibilities

  • Ensure data quality during acquisition and processing stages including collection and transformation.
  • Research and apply appropriate statistical methods and algorithms for data analysis.
  • Oversee the development of high-end analytic models and manage project timelines.
  • Validate production data and analyze models post-implementation.
  • Collaborate with clients to diagnose issues and apply data mining techniques for model behavior analysis.
  • Support pre-sales activities and assist in customer model construction and post-implementation.

Benefits

  • Inclusive culture aligned with core values: ownership, customer delight, respect.
  • Opportunities for professional development through unique strengths and learning experiences.
  • Highly competitive compensation and rewards for performance.
  • People-first work environment promoting work/life balance and social interaction.
  • Employee resource groups and social events to foster camaraderie.
Full Job Description
The Opportunity

In this role, you will be a member of a highly talented team of engineers and scientists enabling advanced predictive models deployed into cloud-based real-time transaction processing systems. You will be a contributor to FICO products and strategic initiatives that involve analytic innovation with a strong focus on building, testing, and deploying advanced analytics for the FICO Platform. You will have the opportunity to build industry leading solutions and stay at the forefront of modern AI and ML technologies.

What You'll Contribute
  • Work with large amounts of real-world data and ensure data quality throughout all stages of acquisition and processing, including data collection, normalization and transformation.
  • Research and select appropriate statistical methods and computational algorithms.
  • Build and/or oversee teams building high-end analytic models for relevant problems using statistical modeling techniques. Manage these projects under time constraints, working with other teams within FICO to provide high quality support to enable integration and deployment of analytics software and solutions.
  • Assist with model go-lives by performing production data validations and analysis of models in production.
  • Assist with client meetings to investigate and resolve problems. Apply data mining methodology in thorough analyses of model behaviors and provide support for customer meetings, model construction and pre-sales. May also participate in post-implementation support.


What We're Seeking
  • MS or PhD degree in computer science, engineering, physics, statistics, mathematics, operations research or natural science fields with significant years of hands-on related experience in predictive modeling and data mining.
  • Experience analyzing large data sets and applying data-cleaning techniques along with performing statistical analyses leading to the understanding of the structure of data sets.
  • Proven ability to lead cross functional teams on medium to large scale projects and handling client interactions requiring strong communication skills.
  • Strong scripting experience using Perl, Bash, or similar.
  • Experience working with two of the following: Java, Python, C++ or C.
  • Proven and demonstrable experience with at least three of the following: neural networks, logistic regression, non-linear regression, random forests, decision trees, support vector machines, linear/non-linear optimization.
  • Prior experience with real-time payment transactional data, including understanding of data field availability, timing, and format requirements for downstream analytics, is a plus


Our Offer to You
  • 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: $122,500 to $192,500 with this range reflecting differences in candidate knowledge, skills and experience.


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About Fair Isaac Corporation

Fair Isaac Corporation, also known as FICO, is a data analytics company that provides credit scoring services and decision management solutions to businesses in various industries. The company was founded in 1956 and is headquartered in San Jose, California. FICO's products and services are used by banks, credit card companies, insurance companies, retailers, and other businesses to make data-driven decisions about credit risk, fraud detection, customer acquisition, and more. The company is committed to using advanced analytics and artificial intelligence to help businesses make better decisions and improve their bottom line.
Learn more about Fair Isaac Corporation
Size
3,460 employees
Market Cap
$15.2 billion
Industry
Enterprise Technology
Net Income
$267.9 million
Founded
1956
5 Year Trend
+8.1%
Revenue
$1.3 billion
NASDAQ
FICO
* Ladders Estimates

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