Signifyd aims to help merchants confidently grow their businesses by building trusted relationships with their customers through advanced technology that approves more good orders, protects revenue, and keeps customers happy, while eliminating fraud.
Requirements
- Strong foundation in machine learning theory, statistical evaluation, and experience with supervised/unsupervised learning at scale.
- Proven track record of taking ML projects from research/prototype to high-scale production environments.
- Proficiency in Python, SQL, key ML libraries, and Spark
- Passion for writing well-tested production-grade code
- Experience: 4-6+ years of post-undergrad work experience in a production-grade ML environment.
Responsibilities
- Identify, prototype, and integrate new ML technologies and infrastructure to enhance fraud detection effectiveness and scalability.
- Own the design and implementation of ML pipeline components that accelerate our innovation
- Foster a culture of technical excellence by championing best practices in testing, documentation, model monitoring, and development.
- building, maintaining, and monitoring the production ML models and offline experimentation frameworks
- contribute novel modeling methods, advanced feature engineering, and robust statistical practices
- own the end-to-end lifecycle of high-impact ML projects, from offline experimentation to deployment to production.
- improving model performance, refining our experimentation processes, and ensuring our fraud detection systems are robust, scalable, and scientifically sound.
Other
- Partner with Product, Engineering, and Risk teams to translate business requirements into technical solutions and ensure ML initiatives align with customer needs.
- A strong outcome-oriented mindset—you care about the "why" behind the models and the business impact they create.
- Ability to communicate technical findings clearly to both technical peers and non-technical stakeholders.
- Education: A degree in Computer Science, Statistics, or a comparable quantitative field.
- Previous experience in fraud, fintech, payments, or e-commerce.
