jasontan

the truth is out there!

That sucks. We (Sift Science) have been building something similar for 7 years and aren’t going anywhere. If we can help, please ping me - jason at siftscience dot com

Sift Science | San Francisco, CA | Join the fraud fighting team! https://siftscience.com/ | Onsite

We are a hyper-growth Series C company based in San Francisco. We’re in the business of squashing fraud and other malicious activity for the world’s largest web and mobile businesses. Our next-generation platform is built on proprietary machine learning technology that learns in real time from live activity taking place on the sites and apps of our global network of customers. By integrating our modern REST APIs and automation workflows, our customers not only eliminate risk but also increase revenue and conversion rates through user experience optimization.

Open Roles:

- Data Scientist

- Senior Backend Engineer

- Senior Full Stack Engineer

- Senior Machine Learning Engineer

- Senior Site Reliability Engineer

- Software Mobile SDK Engineer

We’re hiring in our San Francisco and Seattle office and we would love for you to join the team! If interested please visit our careers page and apply https://siftscience.com/careers.

More Info: https://engineering.siftscience.com/ Questions? E-mail Recruiting@siftscience.com

Sift Science | YCS11 | Onsite in SF

Unfortunately, evil exists. Fortunately, we're here to stop it! Fraud and abuse plague online businesses of all types, from marketplaces to payment processors, social networks to e-commerce stores. As the internet's trust layer, Sift Science's mission is simple yet powerful: make these online experiences faster, smoother, and safer – using the smartest technology around.

Sift Science is hiring for Backend Engineers, SREs, Full Stack Generalists and Mobile SDK Eng.

Curious about what we're working on? Visit us at engineering.siftscience.com to learn more.

Apply at www.siftscience.com/careers

CEO and cofounder of Sift Science here. I think we are complementary, actually. Wallarm focuses on security vulnerabilities (like a more automated HackerOne), and we focus more on "application abuse" (user-level fraud).

Great job, Wallarm!

Sift Science | YC11 | Onsite, San Francisco, CA

Unfortunately, evil exists. Fortunately, we're here to stop it! Fraud and abuse plague online businesses of all types, from marketplaces to payment processors, social networks to e-commerce stores. As the internet's trust layer, Sift Science's mission is simple yet powerful: make these online experiences faster, smoother, and safer – using the smartest technology around.

We just raised $30M in Series C funding from Insight Ventures -- join us in making the internet a better place!

We are hiring for:

-Senior Backend Engineers

-Senior Site Reliability Engineers

-Senior Full Stack Engineers

-Sales people

-Business Operations

-Web Developers

Email: Recruiting@siftscience.com

Hi Josh, Jason here, CEO of Sift Science. Would love to hear your feedback on what we could do better, whether publicly or privately - jason at siftscience dot com. We want to do better.

San Francisco | Sift Science (YCS11) | Onsite

Sift Science (YCS11) is hiring Machine Learning Engineers, Console Engineers, Engineering Managers, DevOps Engineers and more!

https://siftscience.com/jobs

Full-time. Sift Science uses real-time machine learning to fight online fraud. It's a problem that cost U.S. merchants > $12B last year with 70% being a result of organized crime. We are currently seeking ML engineers to join our team to work on our diverse and exponentially growing dataset to employ large-scale, online machine learning and model millions of unique features. Sift is a tight-knit team that likes board games, yummy food, and solving challenging technical problems. Check out https://siftscience.com/jobs or ping us at recruiting@siftscience.com for more information :)

Sift Science (YC S11) | San Francisco | Full time | Onsite

Sift Science uses real-time machine learning to prevent and predict online fraud. We are a lean group of driven and collaborative people passionate about bringing machine learning into the real world. Do you despise evil? Believe fraud must be eradicated from the internet? If so, Sift Science must be for you.

We are hiring for engineers (front-end, machine learning and everything in between), sales, marketing and business experts.

Visit jobs.siftscience.com or reach out directly to lynda@siftscience.com for details/questions.

Sift on!

Sift Science (http://siftscience.com) CEO here. We focus on helping online businesses like Airbnb, Match.com, and OpenTable automate away their fraud problems with realtime machine learning. Many of our customers use us an additional layer of protection in addition to what Stripe or any other payment gateway offers. I'm happy to answer any questions about fraud.

Hi Ben, CEO of Sift Science here. Happy to share what we do to secure and protect your data - jason at siftscience dot com

Thanks for the kind words.

Thanks for using us! If you need extra help feel free to email me - jason at siftscience dot com

We at Sift Science (http://siftscience.com) might be able to help. Feel free to email me at jason at siftscience dot com

Even if you don't use us, we published some articles to help merchants new to dealing with fraud: * https://siftscience.com/sift-edu/fraud-basics * https://siftscience.com/sift-edu/prevent-fraud

Thank you for the recommendation!

Hi Mark, CEO of Sift Science here. Thanks for giving us a shot. Please don't hesitate to ping me if you need any help or we're not delivering to your expectations. jason at siftscience dot com

Sift Science, San Francisco, is Hiring Machine Learning Engineers

Full-time, Onsite. Sift Science uses real-time machine learning to fight online fraud. It's a problem that cost U.S. merchants > $12B last year with 70% being a result of organized crime. We are currently seeking ML engineers to join our team to work on our diverse and exponentially growing dataset to employ large-scale, online machine learning and model millions of unique features. Sift is a tight-knit team that likes board games, yummy food, and solving challenging technical problems. Check out https://siftscience.com/jobs or ping us at recruiting@siftscience.com for more information :)

Sift Science (YCS11) is Hiring Machine Learning Experts San Francisco, Full-time, https://siftscience.com

Sift Science uses real-time machine learning to fight online fraud. It's a problem that cost U.S. merchants > $12B last year with 70% being a result of organized crime. We are currently seeking ML engineers to join our team to work on our diverse and exponentially growing dataset to employ large-scale, online machine learning and model millions of unique features. Sift is a tight-knit team that likes board games, yummy food, and solving challenging technical problems. Check out https://siftscience.com/jobs or ping us at jobs@siftscience.com for more information :)

Hi JackFr,

Jason here, op and CEO of Sift Science. You have a point, but do keep in mind that the TV is going to a bad customer -- one that won't reward Best Buy with repeat business (perhaps just more fraud) and won't spread positive word of mouth (except to let other fraudsters know that Best Buy is a great fraud target). So there is some "lose" in shipping the TV to a bad customer, different from shipping it to a good customer. Does that make sense?

thank you, will!

hi ripberge, would love to hear what was confusing about our documentation, and what bugs you've seen. we're always looking to improve the customer experience - can you email me (jason at siftscience dot com)

hi suprgeek, I'm the op (and CEO of Sift Science). it's true - we do not have any rules in our product. we believe that rules can serve as an effective short-term solution, or for special cases, e.g. prematurely blocking a massive wave of fraud from an IP address because you know it's coming.

but, rules are rather easy for fraudsters to circumvent, and they require merchants to play whack-a-mole. with today's technologies, it's easier than ever to analyze massive amounts of data, and we believe that machine learning can go a really long way in detecting fraud.

does that make sense? happy to discuss further, and we'd be happy to put you in touch with our customers if you'd like to hear more about our results.

Jason here, op (and CEO of Sift Science). It wasn't quite clear in the article - in the example of the stolen television, there is a key difference between Best Buy and bestbuy.com. In the latter, the merchant takes the hit on fraud (e.g. $1000 will be subtracted from the bank account of bestbuy.com), whereas in the former, the merchant is off the hook for fraud. This is one of the key differences between Card Present (offline) and Card Not Present (online) transactions.

I've contacted the reporter to try and clear this up.

Sift Science - San Francisco, CA. Full-time.

Sift Science (http://siftscience.com) uses large-scale machine learning to fight online fraud. It's a problem that cost U.S. merchants > $10B last year, and 70% of it is organized crime. Attacks have rapidly evolved in breadth and depth, but current rule-based systems don't scale. We're looking for engineers of all flavors -- distributed systems, web development, data visualization, site reliability, and of course, machine learning. We're a tight-knit team that likes board games, yummy food, and solving challenging technical problems. Check out https://siftscience.com/jobs We're also looking for integration engineers, technical writers, a head of marketing, a head of integration engineering, an office manager, and a finance person. jobs+hn at siftscience dot com

Sift Science (http://siftscience.com) - San Francisco, CA

Sift Science uses large-scale, real time machine learning to fight online fraud. It's a problem that cost U.S. merchants > $10B last year, and 70% of it is organized crime. Attacks have rapidly evolved in breadth and depth, but current rule-based systems don't scale.

We're looking for engineers of all flavors -- distributed systems, web development, data visualization, and of course, machine learning. We're a tight-knit team that likes board games, yummy food, and solving challenging technical problems. Check out https://siftscience.com/jobs We're also looking for account managers, solution engineers, sales, and a product marketing leader. Feel free to email me personally - jason at siftscience dot com

Hey there, I'm the CEO of Sift Science. Unfortunately, callmeed is spot on -- law enforcement typically won't get involved unless it's in the tens of millions of dollars, at least. Even trickier if it's across international borders.

This means that you're left to defend yourself. Typically, you'll start implementing some basic verification and rules in your code base. For example, "if num_credit_cards_per_destination > 5; flag_as_suspicious()". But, it's tough to be accurate with this approach, so you'll want to manually review activity flagged by rules, so that you don't insult your good customers. As your business grows, it's more challenging to scale these fraud detection rules and manual review operations. While adding more verification helps, it does negatively impact the experience for innocent customers. It's a delicate balance.

I wish I had better news. In some sense, seeing fraud means that you're on the map. Unfortunately that means you'll only attract more and more attention as your business grows. I'm happy to be a resource, even if we don't work together - jason at siftscience dot com.

Sift Science - San Francisco, CA. Full-time.

Sift Science (http://siftscience.com) uses large-scale machine learning to fight online fraud. It's a problem that cost U.S. merchants > $10B last year, and 70% of it is organized crime. Attacks have rapidly evolved in breadth and depth, but current rule-based systems don't scale. We're looking for engineers of all flavors -- distributed systems, web development, data visualization, and of course, machine learning. We're a tight-knit team that likes board games, yummy food, and solving challenging technical problems. Check out https://siftscience.com/jobs We're also looking for account managers, integration engineers, and someone to lead our B2B marketing efforts. jobs+hn@siftscience.com

Sift Science - San Francisco, CA. Full-time.

Sift Science (http://siftscience.com) uses large-scale machine learning to fight online fraud. It's a problem that cost U.S. merchants > $10B last year, and 70% of it is organized crime. Attacks have rapidly evolved in breadth and depth, but current rule-based systems don't scale. We're looking for engineers of all flavors -- distributed systems, web development, data visualization, and of course, machine learning. We're a tight-knit team that likes board games, yummy food, and solving challenging technical problems. Check out https://siftscience.com/jobs We're also looking for account managers, integration engineers, and someone to lead our B2B marketing efforts. Feel free to email me personally - jason at siftscience dot com

Sift Science - San Francisco, CA. Full-time.

Sift Science (http://siftscience.com) uses large-scale machine learning to fight online fraud. It's a problem that cost U.S. merchants > $10B last year, and 70% of it is organized crime. Attacks have rapidly evolved in breadth and depth, but current rule-based systems don't scale. We're looking for engineers of all flavors -- distributed systems, web development, data visualization, and of course, machine learning. We're a tight-knit team that likes board games, yummy food, and solving challenging technical problems. Check out https://siftscience.com/jobs We're also looking for our first product manager, account managers, integration engineers, and someone to lead our B2B marketing efforts.

Feel free to email me personally - jason at siftscience dot com

Java and Ruby, with a bit of Python on the side!