Job Description
As a Staff Data Scientist at Zendesk, you will be joining the Abuse Prevention team and be responsible for developing machine learning and AI approaches that help us understand and identify anomalous and abusive activity. The role involves collaboration with Data Analysts, Software Engineers, and product teams to share valuable insights and models that can be used to keep Zendesk's customers safe and prevent product abuse.
Key Responsibilities
* Analyze large volumes of behavioural and user interaction data to identify patterns of abuse
* Develop viable detection models and techniques using offline data that can inform and guide engineering implementation in production
* Improve detection accuracy for existing spam detection projects while minimizing false positives
* Build robust monitoring dashboards and alerting systems to track abuse metrics
* Collaborate with cross-functional teams to implement prevention strategies based on data insights
* Know the latest trends in fraud detection and security analytics
* Mentor data analysts and provide technical leadership on projects
* Collaborate with our Security teams on emerging data challenges and provide data science expertise
Required Qualifications
* Advanced degree in Computer Science, Statistics, Applied Mathematics, or related quantitative field, or equivalent practical experience
* 5+ years of experience applying ML/AI, ideally in areas such as fraud detection, anomaly detection, or security
* Strong programming skills in Python, R, or similar languages, with proficiency in data processing frameworks
* Experience with a variety of ML techniques, including supervised and unsupervised learning methods, and advanced data analysis techniques, such data mining methodologies
* Experience with time series analysis and its application to anomaly detection
* Experience working with large datasets and distributed computing environments
* Strong communication skills with ability to translate complex technical concepts to non-technical collaborators
Preferred Qualifications
* Proven track record implementing anomaly detection systems at scale
* Familiarity with graph-based approaches to fraud detection
* Knowledge of cybersecurity concepts and common attack vectors
* Experience with ML model monitoring and maintenance in production
* Background in SaaS product abuse prevention
* Knowledge of regulatory requirements related to fraud prevention and data privacy
* Experience with real-time streaming data processing
Where We Work:
We're rapidly growing our teams in Lisbon, Portugal. We have exciting next-generation ML products that are already in use by customers in the banking and finance area, online and offline retailers, and even national postal services. You will work with a driven team, passionate about delivering the right experience to end-users using sophisticated and pioneering ML technology.
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Hybrid: In this role, our hybrid experience is designed at the team level to give you a rich onsite experience packed with connection, collaboration, learning, and celebration - while also giving you flexibility to work remotely for part of the week. This role must attend our local office for part of the week. The specific in-office schedule is to be determined by the hiring manager.