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Hands-on experience of machine learning methodologies including advanced analytics tools (such as R and Python) along with applied mathematics, ML and Deep Learning frameworks and libraries (TensorFlow, PyTorch, Keras) and ML techniques.
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Experience with common data science tools such as Python, R, PyTorch, TensorFlow, Keras, NLTK, Spacy, or Neo4j, and a good understanding of modeling platforms such as Azure AutoML, SageMaker, DataBricks, DataRobot, and H2O.ai.
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Deep knowledge of Data Science and related technologies (Python, R, scikit-learn, Tensorflow, PyTorch, etc.) Experience with cloud data warehouses (Snowflake, AWS Redshift, Google BigQuery, Azure Data Warehouse, Databricks Lakehouse, etc.
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Experience with common data science tools such as Python, R, PyTorch, TensorFlow, Keras, NLTK, Spacy, or Neo4j, and a good understanding of modelling platforms such as Azure AutoML, SageMaker, DataBricks, DataRobot, and H2O.ai.
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Preferred skills: NLP, Text mining, Tableau, PowerBI, Databricks, Tensorflow. Preferred skills: NLP, Text mining, Tableau, PowerBI, Databricks, Tensorflow. Spring boot, Microservices, Docker, Jenkins and REST API's experience.
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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.
$126,000 - $216,000 a yearFull-timeExpandApply NowActive JobUpdated 12 days ago - UpvoteDownvoteShare Job
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2+ years of hands-on experience with machine learning tools and libraries (scikit-learn, TensorFlow/Keras, PyTorch, Huggingface, AWS Sagemaker and/or Databricks etc.) 2+ years of hands-on experience with machine learning tools and libraries (scikit-learn, TensorFlow/Keras, PyTorch, Huggingface, AWS Sagemaker and/or Databricks etc.
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Experience with ML, deep learning, TensorFlow. Technically skilled in RPA development (Automation Anywhere (AA), Power Automate Desktop, Blue Prism, etc.) Experience with ML, deep learning, TensorFlow.
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Knowledgeable about deep learning frameworks such as TensorFlow or PyTorch. The Data Scientist is expected to pull data from source systems; explore, validate, clean, and transform the data; leverage a wide range of algorithms and analytical approaches including Business Intelligence (BI), Machine Learning (ML), Natural Language Processing (NLP) and Artificial Intelligence (AI); deliver intelligence with full reproducibility; test and deploy the intelligence products following MLOps and DataOps best practices; and communicate results and insights to key stakeholders.
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Familiar with deep learning platforms (e.g., Keras, Tensorflow) and related classification and segmentation algorithms. Our areas of expertise include remote sensing, geospatial analysis, GIS, soil science, water quality and demand evaluation, agricultural systems, salinity and nutrient management, ecosystem restoration, statistics, and regulatory policy.
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1-2 years of work experience with Databricks, Snowflake, Redshift, or other cloud database management platforms. Experience working with big data distributed programming languages, and ecosystems such as Spark, Hadoop, MapReduce, Pig, Kafka.
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Knowledge of deep learning frameworks with a focus on Microsoft’s Cognitive Toolkit (CNTK), TensorFlow and PyTorch. Azure Data Scientist Associate. Azure AI Engineer Associate. Proficiency in Azure services, especially: ML, Cognitive Services, Azure Databricks, Azure Synapse Analytics.
$135,000 - $185,000 a yearFull-timeExpandApply NowActive JobUpdated 3 months ago - UpvoteDownvoteShare Job
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Experience in developing or using deep learning frameworks (e.g. Tensorflow, Keras, Caffe, PyTorch, ONNX, etc.) XLA, TVM, MLIR, LLVM, OpenAI Triton, deep learning models and algorithms, and LLM designs Strong communication and interpersonal skills along with the ability to work in a dynamic and high distributed team Comfortable working across highly matrixed organizations and navigating cross functional relationships and conflicting priority and challenges.
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Experience with machine learning frameworks such as scikit-learn, TensorFlow, Kera, H20. The Company is a privately held real estate investment firm focusing on the acquisition, asset management and redevelopment of affordable multifamily housing nationwide, as well as value-add real estate in select markets.
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Data science, machine learning, optimization models, Master’s degree 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)Primary Location.
$90,000 - $180,000 a yearFull-timeExpandApply NowActive JobUpdated Today
tensorflow job Title: associate Company: Brainstation in Dallas, TX
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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.
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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).