data scientist


Manager, Data Science - NuData ai-jobs.net

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We are the global technology company behind the world's fastest payments processing network. We are a vehicle for commerce, a connection to financial systems for the previously excluded, a technology innovation lab, and the home of Priceless at https://www.priceless.com/ . We ensure every employee has the opportunity to be a part of something bigger and to change lives. We believe as our company grows, so should you. We believe in connecting everyone to endless, priceless possibilities.


Kurvv closes $1M seed funding round for AI service designed for small businesses

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Artificial intelligence is a big deal for the likes of Amazon, Microsoft and Google -- but what about a small business that can't afford to have a data scientist on staff? That's the niche that Bellevue, Wash.-based Kurvv plans to fill, with a service that takes a company's data and fits it into a pre-trained AI model that may not be perfect, but is good enough to address the problem that needs solving. "We're targeting companies that have data, but don't have the knowledge or the resources to hire data scientists and can't bring in a consultant," Kurvv's CEO, Ryan Lee, told GeekWire. Lee left his post as a data science program manager at Microsoft in May to focus on getting Kurvv off the ground, drawing upon more than 15 years of experience in product management. One of Lee's fellow co-founders is Vince Roche, who co-founded Boost Media, a San Francisco-based ad optimization venture, and now serves as Kurvv's chief technology officer.


3 common roadblocks to maximizing AI and how to bypass them - Dynamics 365 Blog

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If AI is so transformative, why haven't more enterprises embraced it already? Perhaps we've been trying to force a square peg into a round hole by asking people to adapt to AI, instead of the other way around. At Microsoft, we're striving to change that. We believe business users closest to specific problems have the greatest insights in how to solve them. In our Reimagining AI for Microsoft Business Applications blog series, we offer suggestions to help you capitalize on the enormous promise of AI.


5 Step Guide to Scalable Deep Learning Pipelines with d6tflow

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Building deep learning models typically involves complex data pipelines as well as a lot of trial and error, tweaking model architecture and parameters whose performance needs to be compared. It is often difficult to keep track of all the experiments, leading at best to confusion and at worst wrong conclusions. In 4 reasons why your ML code is bad we explored how to organize ML code as DAG workflows to solve that problem. In this guide we will go through a practical case study on turning an existing pytorch script into a scalable deep learning pipeline with d6tflow. The starting point is a pytorch deep recommender model by Facebook and we will go through the 5 steps of migrating the code into a scalable deep learning pipeline.


5 Step Guide to Scalable Deep Learning Pipelines with d6tflow

#artificialintelligence

Building deep learning models typically involves complex data pipelines as well as a lot of trial and error, tweaking model architecture and parameters whose performance needs to be compared. It is often difficult to keep track of all the experiments, leading at best to confusion and at worst wrong conclusions. In 4 reasons why your ML code is bad we explored how to organize ML code as DAG workflows to solve that problem. In this guide we will go through a practical case study on turning an existing pytorch script into a scalable deep learning pipeline with d6tflow. The starting point is a pytorch deep recommender model by Facebook and we will go through the 5 steps of migrating the code into a scalable deep learning pipeline.


AI 50: America's Most Promising Artificial Intelligence Companies

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Artificial intelligence is infiltrating every industry, allowing vehicles to navigate without drivers, assisting doctors with medical diagnoses, and mimicking the way humans speak. But for all the authentic and exciting ways it's transforming the tasks computers can perform, there's a lot of hype, too. As Jeremy Achin, CEO of newly minted unicorn DataRobot, puts it: "Everyone knows you have to have machine learning in your story or you're not sexy." The inherently broad term gets bandied about so often that it can start to feel meaningless and can be trotted out by companies to gussy up even simple data analysis. To help cut through the noise, Forbes and data partner Meritech Capital put together a list of private, U.S.-based companies that are wielding some subset of artificial intelligence in a meaningful way and demonstrating real business potential from doing so. One makes robots that can whir around shoppers to help workers restock shelves. Another scans recruiting pitches for unconscious bias. A third analyzes massive data sets to make street-by-street weather predictions. To be included on the list, companies needed to show that techniques like machine learning (where systems learn from data to improve on tasks), natural language processing (which enables programs to "understand" written or spoken language), or computer vision (which relates to how machines "see") are a core part of their business model and future success. Find all the details on our methodology here.


AI 50: America's Most Promising Artificial Intelligence Companies

#artificialintelligence

Artificial intelligence is infiltrating every industry, allowing vehicles to navigate without drivers, assisting doctors with medical diagnoses, and mimicking the way humans speak. But for all the authentic and exciting ways it's transforming the tasks computers can perform, there's a lot of hype, too. As Jeremy Achin, CEO of newly minted unicorn DataRobot, puts it: "Everyone knows you have to have machine learning in your story or you're not sexy." The inherently broad term gets bandied about so often that it can start to feel meaningless and can be trotted out by companies to gussy up even simple data analysis. To help cut through the noise, Forbes and data partner Meritech Capital put together a list of private, U.S.-based companies that are wielding some subset of artificial intelligence in a meaningful way and demonstrating real business potential from doing so. One makes robots that can whir around shoppers to help workers restock shelves. Another scans recruiting pitches for unconscious bias. A third analyzes massive data sets to make street-by-street weather predictions. To be included on the list, companies needed to show that techniques like machine learning (where systems learn from data to improve on tasks), natural language processing (which enables programs to "understand" written or spoken language), or computer vision (which relates to how machines "see") are a core part of their business model and future success. Find all the details on our methodology here.


AI 50: America's Most Promising Artificial Intelligence Companies

#artificialintelligence

Artificial intelligence is infiltrating every industry, allowing vehicles to navigate without drivers, assisting doctors with medical diagnoses, and mimicking the way humans speak. But for all the authentic and exciting ways it's transforming the tasks computers can perform, there's a lot of hype, too. As Jeremy Achin, CEO of newly minted unicorn DataRobot, puts it: "Everyone knows you have to have machine learning in your story or you're not sexy." The inherently broad term gets bandied about so often that it can start to feel meaningless and can be trotted out by companies to gussy up even simple data analysis. To help cut through the noise, Forbes and data partner Meritech Capital put together a list of private, U.S.-based companies that are wielding some subset of artificial intelligence in a meaningful way and demonstrating real business potential from doing so. One makes robots that can whir around shoppers to help workers restock shelves. Another scans recruiting pitches for unconscious bias. A third analyzes massive data sets to make street-by-street weather predictions. To be included on the list, companies needed to show that techniques like machine learning (where systems learn from data to improve on tasks), natural language processing (which enables programs to "understand" written or spoken language), or computer vision (which relates to how machines "see") are a core part of their business model and future success. Find all the details on our methodology here.


DataRobot Becomes A Unicorn By Selling AI Toolkits To Harried Data Scientists

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"We lived and breathed data science," DataRobot CEO Jeremy Achin says of himself and his cofounder Tom de Godoy. "And we asked ourselves, 'How would we automate our jobs?'" DataRobot wants to make machine learning so simple that a business analyst with basic training can run predictive models without breaking a sweat. The Boston-based startup just raised a $206 million Series E funding round led by Sapphire Ventures to expand the business, which sells software that helps companies across industries develop and deploy in-house AI models. The billion-dollar valuation makes it the highest-ranking of the "picks-and-shovels" startups featured on Forbes' inaugural AI 50 list (meaning the companies that provide tools to help their customers develop their own AI).


The jobs that AI creates – DXC Blogs

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The current wave of artificial intelligence (AI) works by using computer models to simulate intelligent behavior. Machine-learning algorithms are good at learning new behaviors, but bad at identifying when those behaviors are harmful or don't make sense. Companies deploying AI will need a workforce trained to ensure that the technology remains both useful and safe. AI at DXC: Artificial Intelligence is any program that does something that we would think of as intelligent in humans. AI is often based on machine learning and can produce unexpected results.