FICO, also known as Fair Isaac Corporation, is a leading analytics company that provides predictive analytics and business intelligence software solutions to help businesses make smarter decisions. With a focus on using mathematics to predict consumer ...

FICO

\*\*Hybrid Work Mode: 3days WFO/ week

\*\*Experience Range: 6-10years in relevant domain

 

The Opportunity

The need for analytics is everywhere! Be part of our Analytic Science team, solving problems by staying at the forefront of Artificial Intelligence (AI) and Machine Learning (ML) at FICO — a global leader in analytics software whose fraud systems protect more than 4 billion payment cards worldwide. The anomaly detection work in this role sits at the core of that mission: it's the same discipline of behavioural modelling and analytic science that FICO's teams apply across fraud detection, financial crime prevention, and credit lifecycle management for many of the world's largest banks, insurers, and financial institutions. As a Lead Data Scientist, you'll bring deep technical ownership to a hands-on role: you will personally prototype, build, and debug detection models, while also guiding and reviewing the work of others on the team. If you're a problem solver who enjoys tackling tough, real-world data and analytics challenges alongside some of the brightest analytical and engineering minds in the industry, you'll thrive here.

 

 

What We're Seeking

* Advanced degree (MS or PhD) in computer science, engineering, physics, statistics, mathematics, operations research, or a related quantitative field, with 6–10 years of hands-on experience in predictive modelling and data mining.
* Experience analysing large, real-world datasets — including data cleaning and statistical analysis — to develop a genuine understanding of underlying data structure, of the kind FICO works with across fraud and security telemetry.
* Strong applied statistics foundations — distributions, percentiles and empirical CDFs, robust estimators, variance and dispersion measures, and hypothesis testing — applied to high-volume operational data where outliers and skew are the norm rather than the exception.
* Experience establishing behavioural baselines at the right level of aggregation — global, per-entity, or peer-group — and distinguishing genuine anomalies from expected variation in production detection systems.
* Proven experience with a range of modelling techniques, such as neural networks, logistic regression, non-linear regression, random forests, decision trees, support vector machines, and linear/non-linear optimization.
* Practical experience with unsupervised and semi-supervised methods used where labels are scarce — clustering, density- and distance-based outlier detection, isolation forests, dimensionality reduction, autoencoders, information-theoretic measures such as entropy and divergence, and time-series or sequence modelling with calibrated anomaly scoring — along with the judgment to reach for a simpler statistic when one will do.
* Ability to take a detection problem end to end — from raw data and feature construction through model choice, scoring, thresholding, and evaluation — supported by strong programming skills (such as Java, Python, C++, or C) and hands-on Linux experience.
* Background in machine learning and AI is valued, particularly experience in cybersecurity, fraud, or other adversarial domains, and in deploying and monitoring models in production.

 

 

What You'll Contribute

* Design and build detection models over large-scale behavioural and security telemetry — network, endpoint, authentication, and application event data — including problems where labelled examples are scarce, noisy, or unavailable.
* Engineer features from raw, high-volume event logs, including entity-level and time-windowed aggregations, behavioural baselining, temporal and periodicity features, and encoding of high-cardinality identifiers.
* Research and select appropriate statistical methods and computational algorithms, and justify the choice against a simpler baseline.
* Define scoring, calibration, and thresholding approaches that yield prioritized, explainable alerts for downstream analysts, and validate detection quality where complete ground truth does not exist.
* Work with large amounts of real-world data and ensure data quality throughout all stages of acquisition and processing, including collection, normalization, and transformation.
* Build and/or oversee teams building high-end analytic models for relevant problems, managing these projects under time constraints and working with other teams within FICO 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.
* Support clients throughout the engagement — investigating and resolving issues through thorough analysis of model behaviour, and contributing to model construction, pre-sales, and post-implementation support.

 

 

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.