In terms of educational background, PayPal looks for candidates with programming, statistics, economics, and mathematics background with a focus on logical reasoning, data interpretation and a programming mindset.

PayPal, one of the largest online payment processing firm globally, has several data science teams working as Center of Excellence (CoE). For instance, Customer Support Intelligence focuses on research and development to solve NLP issues at scale. On the other hand, Global Data Science, Risk Analytics, Workforce Planning and Forecasting, Merchant & Consumer Analytics are focused on enabling Business/Risk/Compliance and work in a global construct.

We got in touch with V. Chandramouliswaran, Sr. Director, Global Financial Crimes and Customer Protection & Chennai Centre Head, PayPal India, to understand the hiring process for data scientists at the company. 

Skills Required

PayPal mainly focuses on two critical areas while hiring data scientists — functional skills and business. On the functional side, preference is given to candidates with machine learning skills, OpenCV and deep learning. “We also recruit many Business Analytics individuals for whom the data science functional experience requirement is less onerous,” said Chandramouliswaran.

On the business side, experience in payments, banking, risk, customer management, marketing experience is a huge plus.

In terms of educational background, PayPal looks for candidates with programming, statistics, economics, and mathematics background with a focus on logical reasoning, data interpretation and a programming mindset. “However, we have never focused on hiring only from top-tier institutes and are happy to provide opportunities to top talent with the right skill set and passion for solving large scale problems and driving global impact.”

“For us at PayPal, skills and educational background are not as important as experience and exposure,” added Chandramouliswaran. Candidates who have worked with big data, built data science models, solved pressing business problems are preferred.” We look to hire talent that constantly challenges us and pushes us to innovate every day.”

Interview Process

The interview process at PayPal consists of two significant steps:

Screening by talent acquisition and business teams: The first step is screening by the talent acquisition team, who spend about 20-45 minutes explaining the role and understanding the fitment. The business team then spends about 30-45 minutes explaining the roadmap, challenges and cultural fitment. 

Interview process: Post the screening, the candidate goes through an average of five rounds of interviews, each lasting 45-60 minutes covering the fundamentals of data science, ability to program and work with data, ML Libraries (for engineering DS roles) and also address a business case on analytics or model development.

Chandramouliswaran said one of the critical challenges in recruiting is the limited availability of talent for niche skill sets such as NLP and deep learning. Other challenges include a lack of understanding of the digital payments/fintech ecosystem’s nuances and strong business acumen. “Data scientists, in general, go around with a hammer in their hand looking for a nail, and that is the biggest challenge,” he said. 

Chandramouliswaran highlighted the typical mistakes in data scientists’ hiring process:

• Not doing due diligence and completing all key fundamentals of the hiring process. Late hire is always better than a wrong hire.
• Not following a consensus-driven approach.
• Not exposing the candidates to real-time business problems and consumer impacting decisions.

PayPal addresses such pitfalls by evaluating candidates based on their approach to solve real-time problems and implementing the solutions using a small dataset, and exposing the candidate to PayPal’s values, people, work and work culture. 

PayPal primarily relies on traditional ways to recruit data scientists, such as outbound efforts via job boards, campaigns, and referrals. Some of the non-traditional ways include connecting with potential talent during events, meet-ups and networking platforms.

PayPal posts most of the data science roles on its website. 

Interview Questions At PayPal 

The questions depend on the nature of the role. If the role has a heavy data science tilt, then the candidate would be expected to demonstrate a sound understanding of cutting-edge algorithms and will have to engage in a dialogue on ‘what’ works and ‘why’, explains Chandramouliswaran. 

For the Business Analytics role, guesstimate-type questions are asked. “Outside of these, there will be questions on the data skills that a person possesses, an ability to pull, scrub and analyse etc. Needless to say, the candidate needs to be comfortable with a programming language of his or her choice,” he said.

Being A Data Scientist At PayPal 

PayPal believes in inclusive hiring. The skill sets PayPal look for include: 

• Real-world data science problems solving skills
• Question the ‘why’ behind the problem and rejecting a one size fits all approach
• Strive to understand the business
• Always put the team ahead of themselves
• Identify themselves with PayPal’s values

A data scientist at PayPal is expected to carry responsibilities such as problem structuring, data preparation, model & strategy building, model & strategy validation, benefits measurement, and more. “A huge expectation is also in being comfortable with ambiguity. There have been many instances in which problems will be poorly defined, and having the requisite business background to engage in a dialogue to get to a well-defined and impactful problem statement collectively will be a differentiator,” he said.

Paypal provides ample opportunities for data scientists to grow through:

• Using real-time data to solve end-user problems
• Building landscapes that make technology more accessible for users who are not digital natives
• Working with large datasets that support critical decision-making
• Building consumer-friendly products and solutions

“With several data science functions located in India, careers can grow both laterally and vertically,” he said.

Pro Tips

For an analytics professional who wishes to carve out a career in the analytics industry, Chandramouliswaran advises them to be comfortable with data and numbers, and have an innate curiosity and problem-solving skills. “Building the right skill set through courses and competitions along with solving real-world problems will set the stage for people interested in analytics well on their way,” he said. 

The post Data Science Hiring Process At PayPal appeared first on Artificial Intelligence.

Underground cloud services may seem like an oxymoron, but they are quite real, and criminals are using them to speed up attacks and leave very little room for compromised businesses to react.

This is according to a new report from cybersecurity firm Trend Micro, which found terabytes of internal business data and logins – including for Google, Amazon and PayPal – for sale on the dark web.

The logins are sold through access to the cloud logs where they’re stored. As a result, Trend Micro argues, more accounts are monetized and the time from compromise to the account actually being used for nefarious purposes is cut from weeks to days or hours.

Just as businesses enjoy the speed and scalability of cloud services, so do criminals; more computing power and bandwidth allows them to optimize their operations.

Criminals that buy the logs of cloud-based stolen data usually use the data for the purposes of secondary infection, with ransomware being one of the more popular choices.

The report argues that this is a new trend that may gain even more popularity in the future, and even create a “new type of cybercriminal”: an expert in data mining that uses machine learning to enhance pre-processing and extraction of information to maximize usefulness to potential buyers.

Trend Micro believes criminals will focus on standardizing their services and pricing.

The post Hackers are leaning more heavily on cloud resources appeared first on Artificial Intelligence.

A former executive from payment platform PayPal has launched a new deep learning health intelligence platform, Global Care Analytics, to provide claims-and-cost clinical data and predictive analytics for healthcare companies.

The company’s president, Scott Guilfoyle, formerly served as CTO of PayPal, as well as CIO of LendingTree, and other major companies. The move underscores the growing interest in the AI in healthcare space and the emergence of more companies leveraging AI to solve healthcare management issues.

“The CEO, COO or CFO can execute precisely targeted, complex queries on massive data sets with real-time or near-real-time responses by dragging and dropping preset queries into the engine,” Guilfoyle said of the company’s platform. “We have a patented hyper-ingestion engine that can take in a million datasets a minute, which is amazingly fast and accurate. Then our hyper-digestion process uses neural networks to analyze the oceans of data and become more and more accurate as it learns to refine its ability to make connections.”

One of the goals of the founders was to leverage healthcare data that is currently underutilized and “trapped” within organizations, according to CEO Kevin Sullivan.

“When executive management has desktop power to identify gaps in care or delivery of care services, outcome metrics, and the most complete real-time picture of the health of their patient populations, they can speed up more knowledgeable decision making,” he said.

According to Guilfoyle, the company, which plans to follow the analytics technology with Global Care Pay to provide a frictionless billing-claims-reimbursement system, could help eliminate waste in administrative duties, which add up to $147 billion to $510 billion.

The company, Global Care Administrators, is two years old and is raising capital.

The post Paypal exec launches deep learning healthcare healthcare company appeared first on Artificial Intelligence.

his might seem a particularly bad time to be investing in big tech.

President Trump said Tuesday morning that his administration would look into accusations that Google has been secretly working with the Chinese military. The charge came from Peter Thiel, a PayPal (PYPL – Get Report) co-founder and strong supporter of the president.

On the other hand, Bloomberg reported Tuesday that DeepMind, the artificial-intelligence arm of Alphabet,  (GOOGL – Get Report)  might be on the cusp of a major breakthrough in the way new drugs are discovered.

It’s an important innovation. It’s hiding inside the search giant. And it couldn’t come at a better time.

This business is on to something really big. Using data, machine learning and AI, Alphabet managers are incubating vibrant new businesses with innovative business models. One or more of these will become exciting stand-alone businesses.

Some analysts are already doing sum-of-the-parts analyses and they like what they see.

A Jefferies analyst pegged the value of Waymo, Alphabet’s self-driving-car business, at $250 billion in December 2018, according to a story at Business Insider.

Alphabet’s market capitalization is $798 billion, with units including YouTube, Google Search, Google Cloud, Android, the Nest security camera and peripheral businesses, Google Capital, and Stadia, its new video game streaming service set to launch in November.

Together, these parts are probably worth well over $1 trillion.

Until now, the business opportunity for DeepMind was not even on investors’ radar.

The subsidiary has its roots in DeepMind Technologies, a British AI startup that was making progress teaching computers the quirks of human short-term memory. Alphabet acquired the business in 2014.

Two years later, its custom AlphaGo code was so advanced that it became the first computer program to defeat a human in a match of Go, the ancient Chinese strategy game. That human happened to be Lee Sedol, the 18-time world champion.

At the CASP13 meeting in Mexico in December 2018, DeepMind was at it again. This time its human challengers were the brightest minds in biology. The task was predicting the shapes of proteins.

Understanding these structures is important because they govern how diseases form. The problem is there are more possible protein shapes than there are atoms in the universe, Bloomberg notes.

The math has vexed computational biologists for the past 25 years. They have been trying to build more predictive software models for protein folding, the process that leads to proteins taking three-dimensional shapes.

Despite its limited experience with folding, AlphaFold, DeepMind’s entrant, predicted the most accurate structure for 25 out of 43 proteins, taking the top spot over 98 participating teams, according to a report in the Guardian.

For perspective, the second-place team accurately predicted only three of the 43 proteins.

This does not mean Alphabet has an inside track to the next big drug discovery. It doesn’t work that way. Developing new drugs is both expensive and fraught with regulatory hurdles, patient trials and marketing expenses.

Even then, a 2013 study published by Nature Review Drug Discovery found that only 10% of medicines in development ever reach patients.

The business opportunity is increasing those odds.

In The Future Awakens, a November 2017 research study by Deloitte Center for Health Solutions, analysts posit that by 2022 medicine will be predictive, preventative (based on risk), personalized and participatory.

Computational biologists in hoodies and jeans will build personalized drug treatments based on what they know about a patient’s individual genomic makeup. Behind the scenes, data scientists using A, will comb through algorithmic models, looking for previously unseen biomarkers.

DeepMind has come out of nowhere to be a major player in that ecosystem, and it is hiding inside Alphabet shares, practically for free.

The parent’s stock trades at 21 times forward earnings and 5.6 times sales. These metrics reflect the consensus view that Alphabet is an advertising business, subject to regulatory attacks.

The regulation is coming. That’s true.

But the story of the stock is its valuable pieces. Investors are fretting about a potential breakup of Alphabet. They should be embracing that possibility. It will lead to much higher stock prices as the value of its businesses comes to light.

Growth investors should consider buying Alphabet shares into any material weakness.

The post Google AI on Track to Revolutionize Medicine appeared first on Artificial Intelligence.