Zscaler (NASDAQ: ZS) accelerates digital transformation so that customers can be more agile, efficient, resilient, and secure. The Zscaler Zero Trust Exchange is the company's cloud-native platform that protects thousands of customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location. With more than 10 years of experience developing, operating, and scaling the cloud, Zscaler serves thousands of enterprise customers around the world, including 450 of the Forbes Global 2000 organizations. In addition to protecting customers from damaging threats, such as ransomware and data exfiltration, it helps them slash costs, reduce complexity, and improve the user experience by eliminating stacks of latency-creating gateway appliances. Zscaler was founded in 2007 with a mission to make the cloud a safe place to do business and a more enjoyable experience for enterprise users.

Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

Craft.co is a supplier intelligence company helping organizations accelerate data-informed business decisions. Our unique, proprietary data platform tracks thousands of real-time signals across millions of companies globally, delivering best in class monitoring and insight into global supply chains, among other company cohorts. Our clients, including Fortune 100 companies, government and military agencies, SMEs, asset management groups, and others, use our technology for supply chain intelligence, market intelligence and related use cases. Through our modular, secure, customizable portal, our clients can monitor any company they are working with and drive critical actions in real-time. We are a well-funded technology company with leading investors from Silicon Valley and elsewhere, but are not your typical data or SaaS startup.

Alexey Grigorev is the Principal Data scientist at OLX group, a Dutch-domiciled online marketplace headquartered in Amsterdam. With a joint master's degree in IT for business intelligence from Erasmus Mundus, Alexey specialised in'Distributed and Large-Scale Business Intelligence.' Along with a stellar career in various firms like Auriga, Luxoft, Simplaex, etc., Alexey also founded DataTalksClub to provide a guiding platform for aspirants looking to make a career in data science. In an exclusive interaction with Analytics India Magazine, he spoke about his data science journey and the challenges that he tackled in his vibrant career. Alexey: Since my university days, I have always been fascinated by anything that involved data – statistics, time series analysis, etc.

I have always worked with data, since high school, a long time ago in a galaxy far far away. Specifically, my background is astrophysics, with a Ph.D. in the subject. I performed astronomical data analysis, modeling, and simulation for 25 years, while also working on data repositories for space science satellite missions at NASA. I became very interested in the scientific discovery opportunities of very large datasets in the late 1990's, at which time I began my quest into machine learning, data mining, and data science. The motivation for me has always been discovery, from my early days until now.

MS MARCO is a collection of large scale datasets released by Microsoft with the intent of helping the advance of deep learning research related to search. It was our first choice when we decided to create a tutorial showing how to setup a text search application with Vespa. It was getting a lot of attention from the community, in great part due to the intense competition around leaderboards. Besides, being a large and challenging annotated corpus of documents, it checked all the boxes at the time. We followed up the first basic search tutorial with a blog post and a tutorial on how to use ML in Vespa to improve the text search application.

ODSC events for 2019 are all wrapped up, and now it's time to start thinking and planning for the next decade. Our first major event of 2020 will be ODSC East April 13-17 in Boston, MA, and we're excited to announce the first fifty speakers out of 280 schedule. ODSC East 2020 is expected to host over 250 speakers. Tom Mitchell is known as the "Father of Machine Learning" having founded the Machine Learning Department at Carnegie Mellon University and led it as Department Head for its first 10 years, teaching many famous students including Andrew Ng. He is a world-renowned researcher in machine learning, artificial intelligence, and cognitive neuroscience.

Next Caller is searching for a data scientist with a contagious enthusiasm for data, a passion for exploratory problem solving, and a fascination with designing cutting-edge machine learning algorithms from scratch. As the Principal Data Scientist, you can expect to play an essential role in creating innovation and excellence in Next Caller's Machine Learning-driven VeriCall platform for real-time call authentication and fraud prevention. In this role, you will be a core part of the Engineering Team, where you can expect trust, respect, collaboration, and humor as you engage in creative exploration within the department that is the lifeblood of our organization. We are seeking someone who is excited by the challenge of staying several steps ahead of fraudsters, and relishes the opportunity to tackle and solve problems that others think are just too difficult. The Principal Data Scientist reports directly to the Vice President of Engineering, and has the opportunity to coach, mentor, and work alongside two talented Data Analysts while working in parallel with the R&D Team.

Artificial Intelligence and Machine Learning are two of the disruptive forces in the industry today. Being in their early stages, they already have the potential to absolutely change the way business decisions are made in the organization. AI/ML favors companies with significant data stores on historic operations. So, it favors companies with scale and those who have invested in ERP, CRM, MES, Sensor Data, etc. and kept historic data. Very small companies and those that have thrown out historic data will be at a disadvantage." Conversations around Artificial intelligence and Machine Learning began in the 1950s. It was then that people started talking about these technologies academically, in relation to the business and what they could generally mean for the development of computer science. In 2019, AI and ML's all-encompassing presence within the business operations have transformed them from merely a niche solution to a mainstream disruptive force. Anil Arora, Principal Data Scientist, SAS India notes that an explosion of data sources, low costs of computational power and advancements in methods and algorithms are the catalysts that have led to the advancement of AI and ML over the last decade. Artificial intelligence is poised to unleash the next wave of digital disruption, and companies should prepare for it now. AI promises benefits, but also poses urgent challenges that cut across firms, developers, government, and workers. The workforce needs to be reskilled to exploit AI rather than compete with it."

"When you start looking at connecting points, those intersections across the different siloed data collections and siloed departments, you might just be amazed at what you could discover." Most businesses have siloed departments that are pursuing digital transformation independently, creating "islands of innovation" throughout the enterprise. This episode discusses how to unify these efforts into a cohesive strategy to develop an intelligent enterprise. What could your business do if you had total control over your data? In NASA's case, a data transformation is what made the Hubble telescope and countless other amazing discoveries possible. Our guest this episode, Kirk Borne, was there when it happened. He began his career as an astrophysicist, combining datasets from multiple fields in unprecedented ways. Now Kirk's work is more down to earth. In this episode, he shares how businesses can "democratize data" across the enterprise. And he explains how data transparency plus intelligent analytics can lead to new efficiencies, better customer experiences, even entire new business models. "The key is this concept of the culture of experimentation, that you allow people to experiment with data." Kirk Borne is a Principal Data Scientist and Executive Advisor at Booz Allen Hamilton.