Security Research

We offer the industrys only platform that fuses customer identity and anti-fraud solutions customer identity management identity verification and fraud prevention.

We sell to industries with large consumer-facing businesses such as: banking financial services insurance fintech gaming ecommerce/retail telco / media utilities etc.

About the Role:

Transmit Security is a top player in its field and is heading into an exciting this role you will take an integral part in researching designing and improving advanced fraud and abuse detection capabilities for a cutting-edge SaaS product.

We are looking for a hands-on security researcher who combines an attacker mindset fraud-domain curiosity strong technical depth and data analysis skills. You will investigate real-world fraud patterns reproduce attacker techniques analyze telemetry enrich our data collection capabilities discover new security signals and work closely with product data and engineering teams to turn research insights into production-grade detection and prevention mechanisms.

This role is especially relevant for researchers with experience in browser technologies mobile-native applications or both. You may work on desktop and mobile browsers browser APIs browser automation client-side signals native mobile applications Android/iOS behavior mobile security emulators device signals and mobile automation.

You will help expand our visibility into new signals and patterns that improve fraud detection model performance and customer protection.

If you are a talented researcher who enjoys solving complex problems working with messy real-world data and building practical defenses against modern fraud and abuse we want you to join our team.

What youll do:

• Research emerging fraud and abuse techniques across account takeover bots automation phishing social engineering device spoofing emulators remote access tools suspicious network infrastructure and related attack vectors.
• Identify design and validate new data collection opportunities security signals behavioral patterns device indicators browser indicators and mobile app indicators.
• Analyze real-world telemetry customer-provided labels behavioral signals device signals network indicators and attack patterns to identify detection opportunities.
• Reproduce attacker techniques in lab and production-like environments generate telemetry identify detection gaps and translate findings into detection logic.
• Design validate and tune detection and prevention mechanisms with attention to coverage explainability false positives customer impact and production stability.
• Build research infrastructure analysis workflows and internal tools using Python.
• Produce robust data features that can later be used by machine learning models detection logic rules dashboards and customer-facing insights.
• Work with model training pipelines evaluate model behavior compare training and validation results and help determine whether new features improve detection quality.
• Collaborate closely with data science engineering and product teams to take ideas from research hypothesis through validation implementation monitoring and production feedback.
• Stay up to date with fraud trends attacker tooling automation frameworks browser abuse techniques mobile abuse techniques malware behavior phishing techniques and underground ecosystem developments.
• Communicate findings clearly to technical and non-technical stakeholders including recommended actions tradeoffs expected impact and model/detection quality considerations.

What youll need:

• At least 3 years of experience in security research fraud research detection engineering threat research mobile security research browser security research or a similar hands-on technical role.
• Bachelors degree in Computer Science Cybersecurity Data Science or a related field or equivalent hands-on experience.
• Strong hands-on experience with at least one of the following areas:
• Desktop or mobile browsers browser APIs browser automation browser fingerprinting web signals or client-side web security.
• Native mobile applications for Android or iOS mobile OS behavior device signals emulators mobile automation app instrumentation or mobile security.
• Strong understanding of web technologies mobile technologies APIs application behavior and modern attack techniques.
• Strong Python skills and experience building research tools automation data analysis workflows detection prototypes or feature engineering pipelines.
• Experience analyzing messy real-world data investigating anomalies validating hypotheses and drawing practical conclusions from incomplete information.
• Familiarity with machine learning training and validation concepts such as train/test split validation sets overfitting leakage feature quality precision/recall false positives false negatives and model evaluation.
• Ability to produce data features in a structured reliable and model-friendly way.
• Ability to think like an attacker while designing reliable scalable and explainable defenses.
• Strong problem-solving skills independence persistence and a getting things done attitude.
• Excellent communication and interpersonal skills.
• Ability to work closely with engineering product and data science teams and translate research insights into practical product capabilities.

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