Sift is a B2B AI-powered fraud platform that secures digital trust by providing comprehensive fraud prevention solutions. As commerce and financial transactions increasingly move online, the internet facilitates easier and instantaneous transactions but also introduces anonymity between transacting parties. To fully harness the potential of online commerce, mutual trust between parties is essential. Without trust, commerce cannot thrive.
Our team owns the vision, research and development of machine learning models and explainability to solve customer pain-points. By providing clear and accurate assessment of risk at multiple points across the entire end-user journey such as login, account creation and payment transactions, we enable merchants to eliminate fraud losses, and create adaptive, frictionless experiences for low risk activities. We believe that machine learning is THE way to empower internet-scale businesses to prevent Account Creation Fraud, Account Takeover (ATO) and Payment Fraud. Our solutions are meticulously designed to detect and mitigate risks associated with various types of fraud, ensuring a secure and trustworthy environment for online commerce.
Join our innovative team and be at the forefront of AI/ML driven fraud prevention technology. Here, you will have the opportunity to work with machine learning models, innovate, and develop solutions that make a real impact by helping everyone trust the internet. If you are passionate about creating safe online experiences and thrive in a collaborative environment, we would love to hear from you.
What you’ll do
Research and apply the latest machine learning algorithms to power our core business product
Build offline experimentation systems used to simultaneously evaluate tens of thousands of models
Work on evolving Sift’s ML models and architecture.
End-to-end design & prototyping a wide range of technologies.
Scale machine learning pipelines used to produce thousands of models derived from terabytes of data
Build systems that automatically explain how a model arrived at a prediction
Use data science techniques to analyze fraudulent behavior patterns
Collaborate with other teams to build new ways to use machine learning within Sift
Generate and execute on ideas to provide customers with meaningful and actionable insights to identify and prevent fraudulent behaviors and transactions
Leverage anomaly detection algorithms to identify unusual behaviors for customer traffic patterns
What would make you a strong fit
Practical understanding of machine learning and data science concepts, and a track record of solving problems with these methods
4+ years of experience working with production ML systems.
3+ years experience working with large datasets using Spark, MapReduce, or similar technologies
5+ years experience building backend systems using Java, Scala, Python, or other language
Experience training machine learning models end-to-end
Strong communication & collaboration skills, and a belief that team output is more important than individual output
Degree in Statistics, Machine Learning, Computer Science, Electrical Engineering, 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 packages in Python or another programming language
Advanced degree in Statistics, Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, Operations Research, or a related field
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.
Sift is a B2B AI-powered fraud platform that secures digital trust by providing comprehensive fraud prevention solutions. As commerce and financial transactions increasingly move online, the internet facilitates easier and instantaneous transactions but also introduces anonymity between transacting parties. To fully harness the potential of online commerce, mutual trust between parties is essential. Without trust, commerce cannot thrive.
Our team owns the vision, research and development of machine learning models and explainability to solve customer pain-points. By providing clear and accurate assessment of risk at multiple points across the entire end-user journey such as login, account creation and payment transactions, we enable merchants to eliminate fraud losses, and create adaptive, frictionless experiences for low risk activities. We believe that machine learning is THE way to empower internet-scale businesses to prevent Account Creation Fraud, Account Takeover (ATO) and Payment Fraud. Our solutions are meticulously designed to detect and mitigate risks associated with various types of fraud, ensuring a secure and trustworthy environment for online commerce.
Join our innovative team and be at the forefront of AI/ML driven fraud prevention technology. Here, you will have the opportunity to work with machine learning models, innovate, and develop solutions that make a real impact by helping everyone trust the internet. If you are passionate about creating safe online experiences and thrive in a collaborative environment, we would love to hear from you.
What you’ll do
Research and apply the latest machine learning algorithms to power our core business product
Build offline experimentation systems used to simultaneously evaluate tens of thousands of models
Work on evolving Sift’s ML models and architecture.
End-to-end design & prototyping a wide range of technologies.
Scale machine learning pipelines used to produce thousands of models derived from terabytes of data
Build systems that automatically explain how a model arrived at a prediction
Use data science techniques to analyze fraudulent behavior patterns
Collaborate with other teams to build new ways to use machine learning within Sift
Generate and execute on ideas to provide customers with meaningful and actionable insights to identify and prevent fraudulent behaviors and transactions
Leverage anomaly detection algorithms to identify unusual behaviors for customer traffic patterns
What would make you a strong fit
Practical understanding of machine learning and data science concepts, and a track record of solving problems with these methods
4+ years of experience working with production ML systems.
3+ years experience working with large datasets using Spark, MapReduce, or similar technologies
5+ years experience building backend systems using Java, Scala, Python, or other language
Experience training machine learning models end-to-end
Strong communication & collaboration skills, and a belief that team output is more important than individual output
Degree in Statistics, Machine Learning, Computer Science, Electrical Engineering, 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 packages in Python or another programming language
Advanced degree in Statistics, Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, Operations Research, or a related field
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.
Sift