Data scientists have some practices and needs in common with software developers. Both data scientists and software engineers plan, architect, code, iterate, test, and deploy code to achieve their goals. For software developers this often means custom coding applications and microservices; data scientists implement data integrations with dataops, make predictions through analytical models, and create dashboards to help end users navigate results. Devops engineers looking to automate and collaborate with operational engineers should expand their scope and also provide services to data scientists as part of their charter. Larger organizations with multiple data science teams may invest in data science platforms such as Alteryx Analytics, Databricks, and Dataiku that provide a mix of tools for developing, testing, and deploying analytical models.
Amazon's Intelligent Cloud Control Group in Berlin is looking for a Machine Learning Engineer to develop large-scale machine learning implementations that will revolutionize the way Amazon manages its cloud computing infrastructure. With an ever-growing number of fleets, developers, customers, products, marketplaces, sellers, and businesses the Amazon service graph is one of the largest and most complex tech ecosystems in the world. We are building an Intelligent Cloud Control system that enables Amazon businesses (Retail, Amazon Video, Kindle, and more) to accelerate innovation in the cloud. As a Machine Learning Engineer in the Intelligent Cloud Control team, you'll collaborate with scientists to develop and evaluate machine learning models using large datasets such as orders, website traffic, monitoring telemetry, and logs from every host at Amazon. You will own scaling-up successful prototypes and implementing a reliable automated production workflow for the model.
Deepnote, a startup that offers data scientists an IDE-like collaborative online experience for building their machine learning models, today announced that it has raised a $3.8 million seed round led by Index Ventures and Accel, with participation from YC and Credo Ventures, as well as a number of angel investors, including OpenAI's Greg Brockman, Figma's Dylan Field, Elad Gil, Naval Ravikant, Daniel Gross and Lachy Groom. Built around standard Jupyter notebooks, Deepnote wants to provide data scientists with a cloud-based platform that allows them to focus on their work by abstracting away all of the infrastructure. So instead of having to spend a few hours setting up their environment, a student in a data science class, for example, can simply come to Deepnote and get started. In its current form, Deepnote doesn't charge for its service, despite the fact that it allows its users to work with large data sets and train their models on cloud-based machines with attached GPUs. As Deepnote co-founder and CEO (and ex-Mozilla engineer) Jakub Jurových told me, though, he believes that the most important feature of the service is its ability to allow users to collaborate.
When it comes to organizational transformation and turning AI and machine learning from science fiction into a reality that drives day-to-day decisions, it's much easier said than done. Our report, Adopting AI in organizations, which surveyed more than 2,000 decision makers worldwide, is a case in point: 99 percent of respondents claimed to have faced challenges implementing AI and analytics initiatives across all three categories studied: technology, organization, and people/culture. Another significant finding was that 87 percent of respondents faced more people/culture challenges than technology or organizational challenges. The road to AI adoption hasn't been easy, and it's certainly not over. AI and machine learning (ML) initiatives are increasing in the boardroom, as are the opportunities for the average employee -- including business and domain experts, for example, customer support engineers -- to actually leverage them to make better decisions in their day-to-day work.
Directly is working on an exciting new product, Darwin. Directly Darwin, is the next evolution of the chatbot space. It is the only no-code chatbot that teaches itself, and by bringing together machine learning and a community of diverse and talented people, we believe Darwin is a big step forward for the chatbot industry and the future of customer support. Directly has a great opportunity for a Machine Learning Engineer specializing in Natural Language Processing to join our Data Science team. You will get to work with our unique data, impacting our customers via machine learning and driving revenue.