Careers at Sift Science
Sift Science envisions a world where the online experience for customers is faster, smoother, and safer. Businesses no longer encounter online fraud and abuse by leveraging the power of machine learning.
Sift Science can be segmented according to the various industries in which it is used.
- Digital Cash – Helps to prevent fraud from occurring when transferring cash online or using gift cards. Sift Science intends to maintain the simplicity of digital transactions for customers but adds barriers to make it very difficult for data thieves.
- E-commerce – Sift Science ensures that the online shopping experience is seamless and straightforward. Eliminate chargebacks and prevent multiple credit card testing to stop fraudsters.
- On Demand – The future is mobile and Sift Science will protect it at all costs. Placing orders will be streamlined and quick for customers but it will block out criminals.
- Online Marketplace – Eliminates scammers and spammers from various user-oriented websites, like data sites and file sharing sites. Sift Sciences guarantees that your users have the best experience by getting rid of fake and annoying users who want to steal your real user’s money.
- Payment Gateway – Instantly authorize payments to make the lives of merchants easier. Sift Science gives merchants speed, simplicity, and, above all, security. Sift Science informs merchants how risky a user is. Depending on the risk factor, the payment methods can be tailored to suit their needs.
- Travel – Sift Science protects your business and your loyal travelers from fraud when using accommodation reservation sites, online travel agencies, train and flight reservations, and car rentals. Sift Science identifies the fake users from the real ones, thereby lowering fraud and increasing your revenue.
Sift Science was founded on June 1, 2011 from the Y Combinator incubator program, the world’s most powerful start-up incubator and breeding ground for many tech companies. They publicly launched their product on March 19, 2013. Jason Tan, the Chief Executive Officer (CEO) and co-founder of Sift Science, is a software engineer with an illustrious career prior to establishing his company. He interned at Boeing, worked as a software design engineer at Zillow, worked as an engineer at Optify, and served as the Chief Technical Officer (CTO) at BuzzLabs before conceiving the idea of an online fraud protection scheme.
At the Y Combinator incubator program, he met his fellow co-founder Brandon Ballinger and worked on a product that would help businesses grow while eliminating the threat of frauds. They built a machine learning fraud detection solution where the algorithm of the software will learn what the potential threats were, entirely on its own. It “sifts” the fake and dangerous users from the real ones based on the data.
Sift Science is a big data company as of 2014 according to Fortune Magazine. In March 2014, Sift Science won the MRC METAward in the startup category. In the summer of 2014, Sift Science made a name for itself in the web develop community by publishing a blog post titled “Seven Habits of Highly Fraudulent User.”
Sift Science received a hefty fund totaling $53.6 million in four rounds over the past few years. On September 1, 2011, some of the earliest investors in the seed funding of $1.6 million include Marc Benioff, Max Levshin, and Alexis Ohanian, just to name a few. On March 19, 2013, the company received $4 million in Series A funding led by First Round and Union Square Ventures. On May 14, 2014, the company then received $18 million in Series B funding led by Spark Capital. On July 19, 2016, Sift Science raised $30 million in Series C funding led by Insight Venture Partners. This makes Sift Science of the most successful startups to come out of Y Combinator.
Sift Science has garnered success due to its long list of clients, including OpenTable, Airbnb, Match.com, Zoosk, Remitly, EntroPay, and JackThreads, among many others. Hundreds of companies now rely on Sift Science to ensure that they do not face attacks from fraudsters.
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