Do you want to have a huge impact on the largest eCommerce website? Are you interested in solving cutting edge research problems while impacting all eBay advertising channels? Does working with Big Data, cloud computing, large-scale optimization, probabilistic inference, and machine learning excite you? If you answered yes, the Marketing Science team at eBay is the right place for you. We are looking for rockstar Data Scientists to join our team.
The most important step is undoubtedly the preparation. As much as 80% of a Data Scientist's time is spent in connecting the data, cleansing it, normalising and preparing it for different use cases, reviewing code that doesn't work as it should, across multiple programing and modelling platforms and subsequently questioning their career choice. The remaining 20% is building something new and exciting, Gartner now defines AI as "applying advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take action". True AI should solve problems you don't know exist, but until that is the case you can use AI to identify problems better and faster and then separate to the issues solve them faster but you need Data Scientist to tie the issue and the solution together.
On professional sites, eggheads are discussing how technology such as machine learning or ML has impacted businesses and the prospects of career growth in India. Some others are racking their brains, trying to dissect how machine learning is going to shape the employment scenario in India. The point is that it will certainly impact the employment scenario; the only debate is about just how much and to what extent? Not unnaturally, machine learning is currently a topic of discussion among industry leaders, students, and in academia. The impact is such that ML is consistently in the list of LinkedIn's top emerging jobs every year.
Business today is more than simply matching traditional competitors, it's about exploiting digital technologies to create new opportunities, and being able to repeat this. The economy is quickly going digital and Australian businesses must evolve into Modern Digital Businesses (MDBs) which strategically use intelligence assets to improve operations and deploy new products and services, in order to stay competitive and create value for their customers. A group of digital business leaders recently gathered at ThoughtWorks Live in Sydney and Melbourne, to share their insights into how organisations can take advantage of data to adapt and thrive in the digital economy. This report includes strategic and practical advice taken from the event for any business leader – regardless of their organisation's digital maturity – on best practices for taking advantage of data and driving change. A Continuous Intelligence (CI) framework starts with the process of acquiring data and, with the help of analytics and machine learning, derive insights from it to be able to make confident decisions and actions – which are in turn reviewed and validated, to ensure the organisation continuously improves its decision-making capabilities. Steps organisations can take to apply CI to building an MDB, which is agile and technology-driven are also covered.
When we talk about technology and innovation, India's growth story has always been inspiring. Today, start-ups have evolved, and innovation is the hot button that is driving the nation's business ecosystem. From a business perspective, concepts like e-cars, ridesharing, cab aggregation, hotel aggregation or any kind of shared economy is gaining significant focus with its growing user adoption. These domains have achieved maturity in the growth curve and thus provide the industry with a new set of opportunities and challenges. That is not all, even the stakeholders, customers and the employees in this ecosystem are also in the maturity cycle of the growth curve.
Director of Machine Learning at Walmart San Bruno, California, United States (Posted Jun 9 2019) About the company The Walmart US eCommerce team is rapidly innovating to evolve and define the future state of shopping. As the world's largest retailer, we are on a mission to help people save money and live better. With the help of some of the brightest minds in merchandising, marketing, supply chain, talent and more, we are reimaging the intersection of digital and physical shopping to help achieve that mission. Job description As Director of Machine Learning Science, you will lead a highly innovative team to strategically leverage the vast amounts of data from the World's largest Omni-channel retailer to better serve the Customer. Your primary focus will be building advanced data mining techniques, spearheading statistical analysis aligned to key business goals, and architecting high quality prediction systems to integrate with our Walmart Labs products, using advance machine learning techniques.
We're in the midst of a wave of excitement around AI such as hasn't been seen for a few decades. But those previous periods of inflated expectations led to troughs of disappointment. This time is (mostly) different. Applications of AI such as predictive analytics are already decreasing costs and improving reliability of industrial machinery. Pattern recognition can equal or exceed the ability of human experts in some domains.
There is an ongoing worldwide pop culture phenomenon which has recently engulfed the entire world, and of course you know what I'm talking about: data science! But you probably knew that. While the story in Endgame revolves more or less around the Infinity Stones -- as has the entire MCU for quite a while -- and their role in saving nothing less than the entire universe (or half of it, anyways), the practice of data science actually also has something to learn from their powers. I know you don't believe me, but let's take a look. Don't forget, Thanos was a bit of a data scientist himself.
What do developers actually use Python for? According to a developer survey by JetBrains (which also introduced Kotlin, the up-and-coming language for Android development), some 49 percent say they use Python for data analytics, ahead of web development (46 percent), machine learning (42 percent), and system administration (37 percent). Significant numbers of developers also use the language for software testing (25 percent), software prototyping (22 percent), and "educational purposes" (20 percent). Far fewer chose it for graphics, embedded development, or games/mobile development. This data just reinforces the general idea that Python is swallowing the data-analytics space whole.
Big data, analytics, and machine learning are starting to feel like anonymous business words, but they're not just overused abstract concepts--those buzzwords represent huge changes in much of the technology we deal with in our daily lives. Some of those changes have been for the better, making our interaction with machines and information more natural and more powerful. Others have helped companies tap into consumers' relationships, behaviors, locations and innermost thoughts in powerful and often disturbing ways. And the technologies have left a mark on everything from our highways to our homes. It's no surprise that the concept of "information about everything" is being aggressively applied to manufacturing contexts.