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.

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.

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.

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.

Technology powered by artificial intelligence (AI) is enabling organisations to improve their customer experience and boost loyalty and revenues. The role of customer data has never been more crucial. A recent expert roundtable discussed the importance of personalisation and how data drives smart decision making. It outlined why employees need the right skills and should feel empowered to take action on the insights being generated every day. Excellent data management, powered by AI-enabled platforms, can result in improved customer experience, engagement and loyalty.

Here are three areas you should consider focusing on to set yourself up for success. Data scientists with the right combination of skills are in high demand. But what are hiring teams on the lookout for? As with many roles, both technical expertise and soft skills are important. As data scientist Vin Vashishta wrote, data science without soft skills has "limited value to the business".

According to Dun & Bradstreet's "Sentiment Report," 53 percent of 630 compliance and procurement professionals surveyed said they "strongly agree" or "somewhat agree" AI will improve efficiencies and enhance insight within the compliance and procurement function. Yet, nearly the same proportion (45 percent) admitted to being unsure they had the appropriate skills in place to utilize AI in the next year. The top anticipated areas expected to benefit from AI, according to respondents, include risk and fraud detection; data gathering and validation; risk screening; and account reconciliation. The survey also found that "the top anticipated area expected to benefit from AI differs by industry. Financial services and retail most anticipate improved risk and fraud detection with 60% and 55% selecting this as their top area respectively," the survey said.

At first blush, creating better leads for companies that sell things to other companies may not seem like a big deal in a social sense, but I think it really is. They are really people, people trying to do things. In a lot of cases, they have built really great products, really great services, and yet they fail. I think one of the big reasons they fail is they just have not been able to connect with the market. It could be that they failed to get product market fit right, that they built the wrong solution. I think a lot of times, they've built a great solution, but they just don't have sales capabilities, or marketing capabilities. They don't have that little bit of luck to find those first important customers who are going to pay the bills and help them prove that what they have really works in the marketplace.

AI is being built into more systems and software as organizations attempt to compete in the algorithmic age. With the level of machine intelligence reaching new heights, the number of experts is not growing proportionally. To compensate, AI libraries, APIs, systems and software are becoming easier to use so more people can take advantage of them. However, ease of use does not necessarily diminish risks. At present, there's no minimum competence level one must have to operate an AI system, except perhaps data scientists with graduate degrees in math, statistics or computer science who use the most sophisticated tools.

In January, my colleagues at Dun & Bradstreet issued the results of a recent survey, which found that 40% of polled organizations are adding more jobs as a result of deploying AI. This finding appears to counter fears that AI adoption will reduce the availability of human jobs, with only eight of the 100 survey respondents saying that their organizations are cutting jobs due to AI. The Dun & Bradstreet team polled attendees at the AI World Conference & Expo in Boston last December to glean these findings, which raise a larger question as to how companies are adapting to emerging technologies, such as AI and big data -- especially as we're in the midst of an unprecedented era of digital disruption that is only increasing in intensity. Companies are using new technology to disrupt in new ways. And, as leaders in organizations face the realities of digital disruption, the cost of adopting even a fast-follower strategy on AI is simply untenable.