NATURE OF WORK
As a Senior Anti-Fraud/Security Data Scientist, you will play a pivotal role in safeguarding our company's financial integrity. You will leverage your expertise in data science and machine learning to develop and implement advanced fraud detection models. Your work will directly contribute to mitigating financial losses and protecting our customers.
Core Responsibilitie
s
Feature Engineering and Selectio
- n:Identify, extract, and engineer relevant features from diverse data sources (e.g., customer data, transaction data, behavioral data) to effectively discriminate between legitimate and fraudulent user
- s.Conduct feature selection and dimensionality reduction techniques to optimize model performance and computational efficienc
y.
Model Development and Evaluati
- on:Develop and implement advanced machine learning models (e.g., anomaly detection, supervised classification, time series analysis) to accurately detect and prevent fraudulent activiti
- es.Rigorously evaluate model performance using appropriate metrics (e.g., precision, recall, F1-score, AUC-ROC) and conduct A/B testing to validate effectivene
ss.
Data-Driven Insig
- hts:Analyze raw data to uncover patterns, trends, and anomalies that may indicate fraudulent behav
- ior.Generate actionable insights and recommendations to enhance fraud prevention strateg
ies.
Model Deployment and Monito
- ring:Collaborate with engineering teams to deploy and operationalize developed models into production environm
- ents.Establish robust monitoring and alerting systems to track model performance, detect concept drift, and ensure ongoing effective
ness.
Additional Responsibi
litiesCross-Functional Collabora
- tion: Effectively communicate technical concepts and findings to both technical and non-technical stakeho
- lders.Collaborate with subject matter experts, risk analysts, and operational teams to understand fraud trends, identify emerging threats, and refine prevention strat
egies.
Team Lead
- ership:Mentor and guide data scientists to develop their skills and contribute to the team's s
- uccess.Foster a culture of innovation, experimentation, and continuous learning within th
e team.
REQUIRED QUALIF
- ICATIONSBachelor's degree in Data Science, Computer Science, Statistics, or a relate
- d field.Strong proficiency in Python or R programming la
- nguages.Expertise in machine learning and statistical techniques applicable to security and fraud (e.g., anomaly detection, unsupervised learning, supervised classification, time-series analysis, graph analytics, fraud risk s
- coring).Experience with data mining, data cleaning and feature engineering in security/fraud c
- ontexts.Domain expertise in fraud detection, financial crime, or cybersecurity threat de
- tection.In-depth knowledge of fraud detection methodologies and best practices, including rule-based systems, anomaly detection and behavioral a
- nalyticsFamiliarity with security controls and threat m
- odeling.Experience with risk scoring, alert triage, and investigation workflows; ability to create production ready models with monitoring and explainability conside
- rations.Excellent problem-solving and analytical skills; strong attention to
- detail.Ability to work independently and as part of a team; strong collaboration with security, risk, and engineering stake
- holders.Minimum of 6 years of relevant experience in security/fraud data science or closely related
- fields.Research and development experience is a plus; experience publishing or presenting in security/fraud venues is adva
- ntageousProficiency with security/fraud tooling and platforms is
a plus.
