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Big Data


In Search of Data Science Talent with Dr. Kirk Borne

#artificialintelligence

We have gobs of data, nearly limitless cloud compute, and ever-improving machine learning algorithms, so what on earth is holding companies back from succeeding with big data? "Talent, talent, talent," says Dr. Kirk Borne. "The limiting factor is talent." To be sure, Borne has done more than most when it comes to fostering data science talent. Fourteen years ago, before his recent stint at Booz Allen Hamilton or his new gig at DataPrime, Borne helped create the nation's first data science degree program at George Mason University.


How to build a unicorn AI team without unicorns

#artificialintelligence

How do you start assembling an AI team? Well, hire unicorns who can understand the business problem, can translate it into the "right" AI building blocks, and can deliver on the implementation and production deployment. Except that sightings of such unicorns are extremely rare. Even if you find a unicorn, chances are you won't be able to afford it! In my experience leading Data AI products and platforms over the past two decades, a more effective strategy is to focus on recruiting solid performers who cumulatively support seven specific skill personas in the team.


Data Analyst

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As a Product Data Analyst (f/m) you will partner with our product teams to help measure and understand the value we deliver to our customers. Your insights will be key to enable data driven decision-making of Product Development at Contentful. It lets developers and content creators work in parallel, increasing team efficiency and happiness. Companies such as Co-op, Spotify, Bang&Olufson, N26, Swarovski use Contentful to build their mobile and web products, voice controlled apps and more. We're growing rapidly and are backed by over $150 million in funding from top-tier venture capital firms like Sapphire Ventures, Salesforce Ventures, General Catalyst and Benchmark.


Machine Learning Engineer

#artificialintelligence

This can be demonstrated by a track record of happy, referenceable customers who appreciate your technical acumen and your diligence. This is a hands-on role – be ready to jump in and use the product from your first day. We would be thrilled if you-Have an existing portfolio of projects that demonstrate your ability to successfullyapply ML and Data Science methods to real-world business problems.-Have


A Guide to Selecting Machine Learning Models in Python

#artificialintelligence

Model testing is a key part of model building. When done correctly, testing ensures your model is stable and isn't overfit. The three most well-known methods of model testing are randomized train-test split, K-fold cross-validation, and leave one out cross-validation. Feature selection is another important part of model building as it directly impacts model performance and interpretability. The simplest method of feature selection is manual, which is ideally guided by domain expertise.


Engineering Manager, Big Data and Analytics

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Leading the future of luxury mobility Lucid's mission is to inspire the adoption of sustainable energy by creating the most captivating luxury electric vehicles, centered around the human experience. Working at Lucid Motors means having a shared vision to power the future in revolutionary ways. Be part of a once-in-a-lifetime opportunity to transform the automotive industry. We are looking for a Hands-on Big Data Engineering Manager who is looking for a challenge, enjoys thinking big, and looking to make their mark on an extremely fast-growing company. If building large and building fast, working with a young and very talented team of engineers, and collaborating with the brightest mind in the Automotive industry is what you like, Lucid is the best to experience it.


PySpark & AWS: Master Big Data With PySpark and AWS

#artificialintelligence

Implement any project that requires PySpark knowledge from scratch. Know the theory and practical aspects of PySpark and AWS. People who are beginners and know absolutely nothing about PySpark and AWS. People who want to develop intelligent solutions. People who want to learn PySpark and AWS. People who love to learn the theoretical concepts first before implementing them using Python. People who want to learn PySpark along with its implementation in realistic projects.


Sr. Data Engineer

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Role Description: The Rackspace FinOps group enables cloud users to align their cloud technology adoption with their business strategies. We advise many of the world's largest AWS, GCP, Azure, and other Cloud consumers on topics ranging from cloud architecture to organizational governance to cloud economics, driving efficient cloud adoption and usage for our clients. The FinOps Data Engineer role is an exciting opportunity to build solutions that will help us and our clients turn complex multi-cloud cost and performance datasets into actionable insights. This is an opportunity to make an impact on a fast-growing team. You'll be instrumental in creating new and better analytics and ML solutions, and generally innovate to drive new value for our clients.


Data Engineer

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Confluent is pioneering a fundamentally new category of data infrastructure focused on data in motion. Have you ever found a new favourite series on Netflix, picked up groceries curbside at Walmart, or paid for something using Square? That's the power of data in motion in action--giving organisations instant access to the massive amounts of data that is constantly flowing throughout their business. Our cloud-native offering is designed to be the intelligent connective tissue enabling real-time data, from multiple sources, to constantly stream across the organisation. With Confluent, organisations can create a central nervous system to innovate and win in a digital-first world.


Global Big Data Conference

#artificialintelligence

AI budgets are up significantly over the past year as companies compete to survive and grow market share during the global pandemic, according to Appen, which published its State of AI and Machine Learning report this week. The study also detected a correlation between AI budget size and the likelihood that AI projects will actually be deployed on the one hand, and budgets and the use of external data providers on the other. Now in its seventh year, Appen's State of AI seeks to generate a broad snapshot of AI investments across the United States. The company contracted with Harris Poll to investigate various aspects of AI investments and project management at 500 companies, all of which had at least 100 employees. The growth in AI budgets was perhaps the most compelling result to come out of the study, which had a margin of error of 5%.