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Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Using open source frameworks (for example, scikit learn, tensorflow, torch.
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Much of our work contributes to innovative research in the fields of sensor science, signal processing, data fusion, artificial intelligence (AI), machine learning (ML), and augmented reality (AR.
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Techniques: Statistical Analysis, Visualization, Optimization, Machine Learning, Big Data, Data Warehouse, NLP. Drive the adoption of data science-driven mechanisms and machine learning models to continuously evaluate and improve catalog data quality.
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As a Senior Reliability Engineer, you will play a critical role in ensuring the robustness, availability, and performance of our cutting-edge Data Engineering and Machine Learning Platforms.
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Provide SME support on AI capabilities; predictive analytics, data science, natural language processing (NLP), computer vision, process automation, voice-enabled technology, machine learning, and generative AI.
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Work with large scale structured and unstructured data, build and continuously improve cutting edge machine learning models for Airbnb product, business and operational use cases.
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Experience with the development of enterprise level AI, Machine Learning, and Deep Learning platform involving big data management and GPU compute. Develop novel and accurate NLP algorithms and systems, leveraging Deep Learning and Machine Learning on big data resources.
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We're looking for a Machine Learning Engineer who will build out and design machine learning infrastructure for a team of data & remote sensing scientists, working collaboratively with the Analytics and Software teams to deliver data-driven products and solutions.
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Data management, and implementation of machine learning techniques. (e.g. Collect data, perform complex analysis, interpret results, draw conclusions, and clearly present a recommendation to management) (60.
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The Business Intelligence Analyst is also a member of an innovative, proprietary framework for developing new statistical and machine learning models and advanced products for partners and consumers.
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Areas of work include Sensing Hardware Engineering, Sensing ASIC Architecture, Algorithm Engineering, Machine Learning Engineering, Deep Learning, Firmware Engineering, Software Engineering, Quality Assurance Engineering, and User Studies and Human Factors Engineering.
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Candidates aiming to integrate modern methods of data science (DS), machine learning (ML) and artificial-intelligence (AI) to accelerate advances in their respective research areas are encouraged to apply.
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Examples of relevant research topics/past experience include: modeling of complex socio-technical systems; analysis of large-scale spatiotemporal data; econometrics and causal inference; determinants of human behavior; ensemble forecasting; Bayesian deep learning; generative artificial intelligence; etc.
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Priority will be given to candidates in either one of two areas of special interest: (1) scalable synthesis, analysis and implementation of materials relevant to Quantum Information Science and Engineering (QISE), and (2) development of biomedical imaging materials and related technologies (e.g., computational modeling and machine learning) for disease diagnosis and treatment.
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Utilize expertise in computational biology, machine learning, and bioinformatics to develop and optimize algorithms and models for the analysis of high-dimensional biological data.
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data management machine learning jobs Company: Smart Data Solutions
FEATURED BLOG POSTS
Virtual Reality Job Interviews
With the advent of desktop computers, the arduous task of scouring through weekly job classifieds became a thing of the past. The mid-1990s brought about a new era where job seekers could easily search and apply for jobs online. The introduction of AOL's Instant Messaging feature provided an even faster means for employers and candidates to communicate and schedule interviews. As smartphones became more pervasive in the early 2000s, hiring managers increasingly used phone calls for screening and interviewing candidates. Despite this trend, over 80% of interviews still took place in person.
A Potential TikTok Ban?!
As you may already know, there has been a lot of talk lately about the possibility of a TikTok ban. While this has not yet come to fruition, it's important to consider the implications this could have for businesses and recruiters who rely on TikTok as a platform to market their brand, recruit new talent, and connect with their audience.
The Effects of Workplace Racism and Sexism
One day it's a covert statement to a mother returning to work after maternity leave. Another day it's a lingering gaze at an employee enjoying a culturally rich meal. These microaggressions (or sometimes macroaggressions) can take an employee from a confident, high-performer to one that feels insecure being themselves at work. Your employees engage with people with different ideas and feel most comfortable and valued when they can work without losing their cultural, racial, and gender identity. While most employers know this, why have workplace racism and sexism often been neglected?
When Rage Applying Strikes: How to Identify Unserious Candidates
As the job market remains highly competitive, we have seen a surge in "rage applying." This is when candidates apply to multiple jobs, often without considering whether they are truly interested in the role. Rage applying goes hand-in-hand with quiet quitting. Often, employees want to entertain the thoughts and feelings of leaving their job, but they aren't necessarily serious about leaving yet. Meanwhile, other employees engaging in this trend are actually trying to find a better role. As a recruiter, it can be hard to identify who are the real applicants in a sea full of quiet quitters, but understanding rage applying and identifying red flags will certainly help.
How to Increase Job Ad Exposure
In today's competitive job market, writing quality job ads is critical for attracting top talent to your organization. While networking and candidate referrals are prime real estate for finding qualified candidates, nothing beats the tried-and-true method of writing an extraordinary job ad. But while writing a great job ad is the first step, what's more important is increasing visibility. You could have the most detailed, well-written ad on the internet, but if no one sees it, then you are wasting time (and potentially money!). Employers often believe that job boards are the root of the problem, but you can learn how to increase job ad exposure by tweaking a few steps of your recruitment process.
How to Navigate Hiring Out of State
The job market has shifted significantly in recent years. The accelerated adoption of technology has not only pushed many companies into remote working arrangements but also increased the availability of supporting tools and technologies (i.e., video conferencing and collaboration software).