Synthetic data accurately mimics real-world data. It serves as a placeholder for production data in development and testing workflows and is also used to improve the quality of machine learning algorithms. Common use cases revolve around product development/testing, machine learning, data analysis, and data privacy and security. For example, financial institutions use synthetic data to generate reliable market data for algorithmic trading and risk analysis, while healthcare providers use it to analyze patient data without compromising sensitive patient information. Additionally, synthetic data is used in machine learning algorithms to improve performance and accuracy and thus accelerate the development process.
AppsFlyer is known for its massive backend production and data pipelines. On any given day, thousands of servers are processing 200 billion events and crunching petabytes of data in our cloud. AppsFlyer runs a variety of data analytics and machine learning algorithms on those billions of mobile events to provide mission-critical information and actionable insights to its customers in a company that is known for its high standards and people-obsessed culture. We're looking for a talented individual with a pioneering spirit to join the R&D Analytics and FinOps team and help ensure R&D makes data-driven decisions. The ideal candidate will have strong technical skills, with a passion for slicing and dicing data.
The NWO gravitation project Stress in Action capitalizes on fast technological advances and big data analytics to move stress research from the lab to daily life. You will be part of the data analytic support core (DASC) team. The DASC will develop a variety of big data analytics approaches. Specific analytical questions for DASC include: (1) How can we derive counterfactual predictions of stress outcomes with multiple time-varying stress exposures? More specifically, you will work on novel combinations of joint models for longitudinal and time-to-event data with machine learning techniques.
Data science is a rapidly evolving field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. As the volume, variety, and velocity of data continue to grow, data scientists need sophisticated tools to handle and analyze this data. Data Science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It involves the application of various techniques such as statistics, mathematics, computer science, and domain-specific knowledge to analyze, interpret and make sense of data. Data science can be used in various fields such as business, healthcare, finance, marketing, social media, and many others. The goal of data science is to provide actionable insights that can help organizations make data-driven decisions and optimize their operations.
We are seeking a talented Data Scientist to join the Data Science team. The team is responsible for establishing best practices in the data pipeline as well as building large-scale data analytics and modeling for systematic strategies. The Data Scientist will collaborate closely with portfolio managers, data engineering, and operations teams to develop data cleaning and transformation processes, curate datasets, extract features, and generate signals using statistical and machine learning techniques for large-scale datasets. As a Data Scientist, you will acquire domain expertise for a wide range of financial datasets and conduct EDA to discover patterns, trends, and insights. Additionally, you will contribute to expanding a scalable data science environment that facilitates systematic data research through data and analytics sharing, modeling, dashboard visualization, and backtesting.
Machine learning is the science of getting computers to act without being explicitly programmed. It is currently the most popular form of AI (Artificial Intelligence). Rather than relying on fixed deterministic rules, machine learning programs use algorithms that learn from data and make predictions based on that information. Nowadays, machine learning programs can be executed as stand-along programs or as cloud services. Machine Learning as a Service (MLaaS) refers to cloud services that enable their users to take advantage of machine learning programs without having to build their own infrastructure or having to hire expensive machine learning experts and data scientists.
We put marketers' questions to our community in a new series of articles aiming to provide practical advice and connect business leaders. "I am interested in how others are thinking about delivering more personalised experiences to buyers at scale. I'd love to know how they are increasing the use of information to deliver personalised experiences that will be meaningful to customers." Personalisation has been a salient term in digital marketing for many a year and as the technological shackles continue to loosen for the majority of businesses, it is no surprise that the intelligent use of data represented a common response for delivering personalisation at scale. Artificial intelligence (AI) is perhaps the best-known tech solution for optimising and personalising large datasets, and this technology too was frequently referenced alongside machine learning and automation.
Helping our customers develop at the same time as constructing a extra sustainable, extra inclusive destiny is a hard ask. But when you join Leaf Business Consulting Services, you join a thriving company and become part of a numerous international collective of free-thinkers, marketers and enterprise professionals who're all pushed to apply era to reimagine what's possible. Leaf Business Consulting Services has diverse opportunities and job roles that are reflective of an individual's area of expertise. We offer career opportunities and growth in leading industrial domains – Applications & Technology, Operations & Engineering, Strategy & Transformation, Consulting, Manufacturing, Financial Services, Public Sector, Consumer Goods & Retail, Telecoms, Media & Entertainment, Energy & Utilities, and Services. Our roles are aligned to current & industry relevant skills like Guidewire, Java Full Stack, Data Engineering, Data Science, Machine Learning Engineer, Software Engineering, Software Testing, Software Development, Big Data, Hadoop, Sales And Marketing, Project Management, Manufacturing Engineer, Technical Solutions, Business Partnership, Strategy and operations, Solutions consultant, Enterprise Solutions, Cloud Infrastruture Services, Senior Devops Engineer, UX Engineer, UX Researcher, Pega, S4 Hana, Informatica, Azure, SAP, Sitecore, Salesforce Velocity, NodeJS, Mulesoft. Together, we paintings to convert the world's main agencies whilst sharing understanding and pushing ourselves to do better.It's how we shape amazing careers and deliver innovation that human touch the area needs.
SSENSE (pronounced [es-uhns]) is a global technology platform operating at the intersection of culture, community, and commerce. Headquartered in Montreal, it features a mix of established and emerging luxury brands across womenswear, menswear, kidswear, and Everything Else. SSENSE has garnered critical acclaim as both an e-commerce engine and a producer of cultural content, generating an average of 100 million monthly page views. Approximately 80% of its audience is between the ages of 18 to 40. It is privately held and has achieved high double digit annual growth and profitability since its inception.