Our team helps customers proactively prevent payment fraud and account takeovers. As commerce and financial transactions increasingly move online, the internet has made payments easier and faster—but it has also introduced a layer of anonymity between transacting parties. To fully leverage the power of online commerce, mutual trust between parties is essential. Without trust, commerce cannot thrive.

By providing clear, accurate risk assessments at multiple points in the end-user journey, we enable merchants to eliminate fraud losses while creating adaptive, frictionless experiences for low-risk payments. We believe that machine learning is the key to empowering internet-scale businesses to prevent payment fraud effectively.

What you’ll do

• Research and analyze gaps in our ML models (e.g., false positives and false negatives) and summarize emerging fraudulent behavior patterns.

• Define success metrics and propose targeted improvements to enhance model performance and better serve our customers.

• Collaborate with engineers to build scalable, generalized ML models, features, and training regimes that enable low-latency, large-scale fraud prevention.

• Develop systems that explain how models arrive at predictions, increasing transparency and trust.

• Communicate clearly and effectively to influence stakeholders and gain alignment.

• Leverage anomaly detection algorithms to identify unusual patterns in customer traffic behavior.

What makes you a strong fit

• A practical understanding of machine learning and data science, with a proven track record of applying ML to real-world problems.

• 6+ years of experience working with large datasets using tools such as Jupyter, Pandas, PySpark, PyTorch, or TensorFlow.

• 4+ years of experience applying AI/ML methodologies in a production environment.

• A strong focus on delivering customer value, with a preference for practical solutions over purely theoretical ones.

• Proficiency in Python and experience building analytical tools.

• Excellent communication and collaboration skills, with a belief in the power of team success over individual contributions.

• A degree in Statistics, Machine Learning, Computer Science, Applied Mathematics, Operations Research, or a related field.

Bonus points

• Experience working with scalable, real-time prediction systems in production.

• Familiarity with multiple machine learning or statistical libraries in Python, R, MATLAB, or similar languages.

• Experience evaluating and improving model performance.

• An advanced degree in Statistics, Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, Operations Research, or a related field.

Benefits and perks

• Competitive Compensation: Includes financial rewards, an annual 5% bonus, and stock options.

• Health Insurance Stipend: Support for your medical and health-related needs.

• Sports and Wellness Stipend: Encouraging a healthy and active lifestyle.

• Work From Home Stipend: Support in creating a productive home office setup.

• Education Reimbursement: books, education courses, and conferences to support your professional growth.

• Mental Health Days: Additional paid day-offs to prioritize your well-being.

• Language and Public Speaking Development: English courses and social activities within the company to enhance your communication skills.

A little about us

Sift is the AI-powered fraud platform securing digital trust for leading global businesses. Our deep investments in machine learning and user identity, a data network scoring 1 trillion events per year, and a commitment to long-term customer success empower more than 700 customers to grow fearlessly. Brands including DoorDash, Yelp, and Poshmark rely on Sift to unlock growth and deliver seamless consumer experiences. Visit us at sift.com and follow us on LinkedIn.