We are a fast-growing startup that is disrupting the fraud industry by putting the control back into the hands of digital businesses with an innovative approach that bankrupts the underlying business model of fraudsters. Our fraud and abuse prevention platform combines real-time intelligence, rich analytics and adaptive step-up challenges to progressively diminish the profitability of attacks while adapting to evolving attack patterns. We offer the only fraud solution with a 100% SLA guarantee. The world’s largest brands trust us to protect their customer journey while delivering unrivaled customer experience.
The product platform organization is responsible for the overall fraud and abuse protection product strategy. The focus of the team is to define the product requirements, research the best way to defend against the threat vectors and architect the product to meet the following key requirements: Detection: Detect automated and human fraud / abuse through various methods with high accuracy. The detection must be tightly integrated with the challenge framework so as to adopt the most effective response strategy depending on the risk associated with the request session. The challenge framework is managed by Matt Ford’s Product Challenge organization. Visibility: provide a set of dashboard and reports to customers, partner and our support team to allow them to visualize and analyze the fraud activity detected by the product Serviceability: provide customers the ability tune the detection and response strategy based on the risk score. Also provide customers the ability to craft custom logic to meet their own requirements. The product platform consists of technical product managers and the product research team. The product research team is responsible for coming up with different ways to process data collected on a session in order to differentiate bot from human traffic but also detect human fraud within the human traffic. The team may use different techniques including but not limited to statistical model, unsupervised learning, supervised learning, deep learning, whichever model is the most effective to meet the accuracy requirements.
- A Bachelor or Masters Degree in a highly quantitative field (Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc.) or equivalent experience
- 3+ years of industry experience in predictive modeling, data science and analysis
- Previous experience in a ML or data scientist role and a track record of building ML or DL models
- Ability to code, experiment and fail fast
- Proven track record of successful application of ML to resolve hard problems
- Solid understanding of the world wide web ecosystem, including protocols (HTTP, DNS), coding (HTML, JS, CSS), issues around privacy…
- Effective oral and written communication to peers, leadership, and both technical and non-technical audiences
- Ability to adapt quickly and constructively to changes
- A breadth of technical aptitude to talk shop to our engineers Ambitious, accountable, and be a self-starter
Nice to haves:
- Experience working in the web security space is a plus (fraud, bot management, abuse, web application firewall)
- Background in cybersecurity or fraud
- Startup Experience
- Work with the technical product manager team to review requirement and expected outcomes
- Research and develop new ML models as necessary to meet agreed upon product requirements and goals
- Develop POC as needed and help draft the engineering design to be handed over to the dev team
- Evaluate accuracy of detection methods and propose enhancements as needed
- Research newer internet protocol that could provide additional signals that may be relevant to improve detection for example HTTP/2 or HTTP/3
- Research product workflow change or features that will improve detection and ensure optimal user experience
- Follow evolution of privacy features introduced by browser vendors, assess possible impact for the product and propose necessary adjustment as needed.
- Example of this includes: user-agent deprecation and transition to client-hints Google privacy sandbox and privacy budget proposal, Safari's intelligent tracking prevention, Firefox enhanced tracking prevention
- Get involved in customers calls on occasion to discuss future product directions
- Work with the Customer Success team to assist as needed with complex escalation, training on new detection methods and best practices for effectively defending against attacks
- Occasional travel to customer site as necessary
- Participate in security conference to increase awareness on online fraud and abuse
- Competitive salary, equity, and a robust benefits package includes top-notch medical, dental, vision, life insurance, 401k, commuter benefits and we cover 95% of the cost of employee benefits and 65% of the cost of dependent care coverage!
- We also offer flexible PTO and some WFH opportunities as needed.
Cyber Security Startup