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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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Expertise in Python, R, SQL, statistics, data mining. Open source, Commercial, and Government software packages such as Kafka, Beam, NumPy, Kubeflow, Nvidia Triton, PyTorch, TensorFlow, Weaviate, Neo4j, Grafana, etc.
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In our Data science track we prepare you to get job as one of the following: Python developer, a data analyst, data visualization developer, a statistician, a machine learning engineer or a data scientist.
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Java, Python, ScalaExperience in Natural Language Understanding, Computer Vision, Machine Learning, Algorithmic Foundations of Optimization, Data Mining or Machine Intelligence (Artificial Intelligence.
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Expertise with Google Cloud Platform’s distributed Machine Learning, Data Science and Data Engineering tools (e.g. BigQuery ML, VertexAI, AutoML, Docker, Kubernetes, Kubeflow, Dataproc.
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The Research Data Scientist participates in biomedical research projects using programming, data-mining, statistics, machine learning, and visualization techniques to develop, evaluate, and/or apply algorithms and software for data analysis.
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Experience building and testing challenger models using supervised and/or unsupervised machine learning or other data science modeling techniques. Advanced degree in data science, or other advanced degree with coursework in AI, machine learning, and data science.
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Proficiency in programming languages such as Python, R, or SQL. Strong knowledge of machine learning techniques, statistical analysis, and data visualization tools Tableau, Power BI.
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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 SageMaker, Databricks, and Dataiku.
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At least 6 months experience or academic work using open source programming languages for data analysis. Evaluate open-source and internally-developed modeling and analytics tools using real business data.
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Demonstrated proficiency with data science languages (R, Python, SQL, Julia, SAS) and visualization tools (Tableau, Spotfire, SAS, JMP, RShiny) Bachelor's degree in Computer Science, Applied Mathematics/ Data Sciences/ Data Analytics or related discipline required, advanced degree preferred.
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Technical Expertise : Extensive experience in the complete ML development lifecycle with machine learning frameworks (e.g., TensorFlow, PyTorch and Lightning), statistical modeling, data science, and computational biology tools.
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Desired Background PhD in Computer Science or related field 4-8 years of professional experience with relevant experience building and designing distributed data systems Knowledge of statistics, data science, AI/machine learning, big data management Strong proficiency in Python and an additional language (e.g., C.
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The Data Science team leads the strategy, development and integration of Machine Learning and Artificial Intelligence. Build production ready prototypes for, and iteratively develop, end-to-end data science pipelines including custom algorithms, statistical models, machine learning and artificial intelligence functions to meet end user needs.
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Programming Languages: Python, R, or similar for AI and machine learning development. Proficiency in Python, R, and other programming languages for developing machine learning models.
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FEATURED BLOG POSTS
5 Common Interview Mistakes
Everyone's interview process is unique in some form or fashion. Like most, your interview process is crafted so you can get the most information out of your candidates to increase hiring confidence and make the right hiring decisions. However, there are often small problems in interview processes that could ultimately affect the success of hiring decisions.
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?
4 Ways to Make Your Job Posting More Inclusive
According to a Glassdoor survey,
How to Calculate Net Income
Understanding your finances can be daunting even if you’re good with numbers. Your net income, in particular, is a key metric for determining how well you’re doing financially and whether your current way of operating is sustainable or not.
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: