The opportunity is for a Data Engineer to play a critical role in architecting and developing components forming the Analytics platform, whilst implementing new ideas to solve novel challenges related to geospatial analytics at scale. The Data Engineer will collaborate with Data Scientists to bring geospatial algorithms into production at scale, identify business requirements and opportunities, such as utilising new data sources or ways to process and store data. Working primarily in Python & Scala, the data engineer will gain exposure to a range of technologies including Spark, Kafka, AWS, Airflow, Rust and much more. Our mission is to transform the way humans and machines understand the world. We are doing this by creating a real-time index of reality, enabling billions of machines and trillions of sensors to land, index, share and consume each other's data about the world as they move through it.

Want to work on the cutting edge of IoT and ML? This company operates in the internet of things space, using AI to improve energy efficiency and safety with lighting systems both on the consumer end as well as for cities and businesses. A successful candidate will likely have the following skills and experience: • Graduate degree in Computer Science • Computer vision and image processing research • Patents and/or publications • Extensive industry experience with AI and various ML domains (i.e. computer vision, deep learning, etc.) • Interpersonal skills and experience working in cross-functional teams • Expertise with Python, JavaScript, Java, and/or C, as well as associated ML resources Please register your interest by sending your CV to Alex Sandberg via the Apply link on this page.

Are you looking for a new opportunity in a fast-moving global company with a family feel? A job where you could have an impact? We are looking for an Expert in Artificial Intelligence (AI) to work from our offices in Belgium. The AI Expert will become part of multidisciplinary team supporting our clients by implementing ground segment data analysis and planning optimization systems using AI algorithms and cloud environments in the frame of development activities for ESA.

In this blog post, I touch on topics I will cover in depth in Win the War for AI Talent, currently scheduled for publication on June 27, 2022. Most large enterprises begin their AI transformation by hiring a few data scientists to build prototype models. The AI leaders quickly realize they need to make many other foundational changes to realize the company's AI vision. The big, obvious changes might be building an MLOps infrastructure and creating a new ML governance model. On top of those changes, they also need to handle the million little details and challenges that surface every day.

AI in the workplace is a phrase that tends to stir up angry sentiments. Will we be replaced by machines? Will automation make us redundant? But our tendency towards revelling in dystopian rhetoric has a flaw – some people dream of a utopia instead. Some dreamers collaborate on CSIRO Data61's $12 million Collaborative Intelligence (CINTEL) Future Science Platform, which aims to shift the focus of AI in the workplace and find ways to improve it.

Ted Sergott, EVP, Product Development at PRO Unlimited, is responsible for all aspects of PRO's Vendor Management System, Wand. A Fortune/Deloitte survey indicated that 96% of CEOs consider diversity, equity and inclusion (DEI) a "personal strategic priority," and Gartner, Inc. reported that DEI is the top talent management priority for CEOs. As more organizations prioritize diversity, the challenge is figuring out how to achieve it. For starters, how can we block unconscious bias in the hiring process? How do we widen the talent pool to bring more candidates from historically marginalized groups to jobs, roles and locations where diversity has been a challenge?

Artificial Intelligence provides an opportunity to counter the gender biases that have been permeating history for long. Artificial Intelligence is going to be the next big revolution that would transform the world. Like any other revolution that has reformed the perspective of the world, AI would be a sweeping force that will enable new possibilities in the frontier. But just like any other revolution, the possibility of the contribution by women in this tech world would either be forgotten or will not be acknowledged is definite. If we have to look through history, we will realize how the contribution of women is viewed in shaping the world.

Today, I'm extremely happy to announce the availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that accelerates your machine learning workflows with one-click access to popular model collections (also known as "model zoos"), and to end-to-end solutions that solve common use cases. In recent years, machine learning (ML) has proven to be a valuable technique in improving and automating business processes. Indeed, models trained on historical data can accurately predict outcomes across a wide range of industry segments: financial services, retail, manufacturing, telecom, life sciences, and so on. Yet, working with these models requires skills and experience that only a subset of scientists and developers have: preparing a dataset, selecting an algorithm, training a model, optimizing its accuracy, deploying it in production, and monitoring its performance over time. In order to simplify the model building process, the ML community has created model zoos, that is to say, collections of models built with popular open source libraries, and often pretrained on reference datasets.

The traditional route in recruiting is posting a job, selecting candidates from a mountain of CVs, calling around to arrange interviews, it's the same process every time a job becomes available. When recruiting is done this way it's costly, it takes too much time, important information gets missed, good candidates get overlooked and the candidate experience can be poor. Recruiting in this way leaves too much room for error, one bad hire can cost a business thousands of pounds. AI will automate many tasks and reduce the workload for recruiting professionals as well as making the whole experience better for the candidate. Let's look at how AI can do that.

A relatively small group of savvy executives have strategies in place to harness new business process automation technologies and thereby advance their digital transformation agenda. Meanwhile, a much larger group is closely following the market leaders, to explore their lessons-learned from pilot projects. According to the latest worldwide market study by 451 Research, new survey results suggest most organisations are adopting or considering artificial intelligence (AI) and machine learning (ML) due to its commercial growth benefits, rather than the potential to cut jobs. Almost 50 percent of their survey respondents have deployed or plan to deploy machine learning in their organisations within the next 12 months. Therefore, this paints a more optimistic picture of machine learning adoption than is often portrayed by other industry analysts. "Out of many possible benefits we presented to our survey respondents, 49 percent cited gaining competitive advantage as the most significant benefit they have received from the technology," said Nick Patience, vice president at 451 Research.