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Building and applying data analysis algorithms (data mining, statistics, machine learning, natural language processing, sentiment analysis, text mining, etc.
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Demonstrated experience in data analysis, explanatory and predictive modeling, data manipulation, analytical applications, big data engineering, algorithms, statistics, machine learning, natural language processing, and data visualization.
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10 years full-time Software Engineering work experience, which includes 6+ years of software engineering experience in one or more of the following areas: advertising, recommendation systems, risk/fraud modeling, or natural language processing.
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A Full-time, Director of Data Science - NLP, LLM and GenAI job is available with our client, a leader in risk management solutions leveraging automation and AI/ML, located in NYC. This is a remote role.
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5 years of experience with machine learning algorithms and tools (e.g., TensorFlow), artificial intelligence, deep learning, and/or natural language processing.
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The Department of Information Sciences and Technology (IST) has 44 full-time faculty members with research foci in cybersecurity, data mining and machine learning, natural language processing, human-centered computing, and computing and engineering education.
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Our classes span the full spectrum of computer science and data science such as machine learning, systems, artificial intelligence, algorithms, databases, data analytics, computer vision, graphics, gaming, modeling, natural language processing, and many others.
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Strong knowledge of machine learning algorithms (supervised and unsupervised learning)/models, techniques to predict, natural language processing and computational social science.
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Several faculty members in the EECS department are NSF -funded and publish in top research venues, with concentrations in big data, computer networks, natural language processing, machine learning, and artificial intelligence.
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You'll spend most of your time working with a wide variety of clients to deliver the latest data science and big data technologies and practices to design, build and maintain scalable and robust solutions that unify, enrich and analyze data from multiple sources.
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Our areas of focus include Big Data Analytics, Deep Learning, Reinforcement Learning, Natural Language Processing, Classical Machine Learning, Time Series, Automation, Explainability and Interpretability.
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We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day.
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6 years of experience in software engineering, including experience with Machine Learning (ML) models, ML infrastructure, Natural Language Processing or Computer Vision.
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10 years full-time Software Engineering work experience, which includes 6+ years of software engineering experience in one or more of the following areas: computer vision, natural language processing, foundational model training, reinforcement learning.
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At least 4 years experience in managing high-performing teams in Machine Learning and Data Engineering. You will manage a blended team of machine learning modelers, infrastructure experts, and data engineers.
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5 Common Interview Mistakes
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One part of the job-hunting process that frequently gets overlooked is putting together a list of good references. Most of the time we focus on creating the perfect resume, writing an awesome cover letter, and getting our hands on letters of recommendation. We think about what outfit we’ll wear to the job interview, how we’ll answer those tricky questions, and what our career plan looks like. But, in fact, having multiple references lined up who will speak favorably about you to a potential employer is critical to landing a job. This aspect of job searching really can’t be ignored.
Job Rejection Email Response with Examples
Glassdoor estimates that, on average, there are about 250 applicants for every job vacancy out there. If you’ve ever applied for a job, the odds are that you’ve received the dreaded job rejection email.
Structured vs Unstructured Interviews
The goal of an interview is to evaluate candidates based on their skills, personality, and knowledge. You want to choose the BEST candidate from your candidate pool, so the interview is something you can't mess up. As you begin planning your interview process, one of the major decisions you'll face is whether the interview should be a structured vs unstructured interview. So let's take a dive into the differences and sort out which circumstances warrant which interview process.
How to Describe Your Personality with Examples
Imagine you’re in an elevator with the CEO of your dream company and you get to talking. The conversation is going well and you start to imagine yourself working for their company when the CEO turns around and asks you “tell me a bit about yourself.” Would this catch you off guard or would you be able to give a clear and succinct description of who you are?
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To ATS or not to ATS
As hiring is becoming more analytical and data-driven, companies have found ways to incorporate technology to help hire and recruit more efficiently. ATS, also known as an applicant tracking system, has become one of the most widely adopted technological recruiting tools to date. In fact, according to data from Capterra: