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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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Demonstrated expertise in machine learning, deep learning, and NLP, with a strong portfolio in large language models like GPT-3, BERT, and RAG.Advanced proficiency in Python, with hands-on experience in ML libraries and frameworks such as TensorFlow and PyTorch.
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Implement data processing solutions using Spark, Java, Python, Databricks, Tecton, and various AWS services (S3, Redshift, EMR, Athena, Glue). Deep understanding of data processing, comfortable working with multi-terabyte datasets, and skilled in high-scale data ingestion, transformation, and distributed processing, with strong Apache Spark or Databricks experience.
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Expert ability in operations research and statistical methods and techniques, including deployment analysis and resource allocation modeling, systems/operations analysis, simulation and optimization (linear and nonlinear, predictive analytics (e.g. deep learning, econometrics, time series/forecasting, vehicle routing, and related skills.
$78,721 - $150,000Full-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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2-5+ years of experience in wide range of statistical and machine-learning techniques (e.g. time series analysis, NLP, deep learning and etc.) Conduct research on cutting edge technologies in AI/BI/ML/NLP, alternative data, FinTech, high performance computing (e.g. GPU, quantum computing) to discover the potential opportunities to the business applications.
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Strong experience with machine learning technology, such as: big data stack, Python, R, and visualization techniques. + Advanced degree preferred, with 5+ years of experience in Applied Econometrics, Statistics, Data Mining, Machine Learning, Analytics, Mathematics, Operations Research, Industrial Engineering, or related field preferred.
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Deep technical proficiency with data processing tools and platforms (e.g., Snowflake, AWS), data modeling, coding languages (SQL, Python), and ELT processes. Experience with machine learning and AI technologies applied to unstructured data.
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RNN) in a work setting, not just academic projects Hands-on experience in building and deploying machine learning and statistical models using Python, PySpark, Machine Learning libraries, SQL. Experience in computing/programming skills; proficiency in Python, R, and Linux shell script.
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Oversee the continued development of machine learning including supervised machine learning, unsupervised machine learning, deep learning, optimization solutions, image/visual analytics, and natural language processing.
$210,000 - $350,000 a yearFull-timeExpandApply NowActive JobUpdated 23 days ago - UpvoteDownvoteShare Job
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The Enterprise Model Platform team in the Machine Learning Experience (MLX) organization supports a hosted internal Jupyter Notebook system running critical infrastructure for modeling and exploratory analysis across Capital One. You will get an opportunity and exposure to the thousands of models being trained on the notebook platform by myriad users across Capital One and dive deep into the unique engineering challenges we have around data access, security and scale.
$201,400 a yearFull-timeExpandApply NowActive JobUpdated 1 month ago - UpvoteDownvoteShare Job
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Advanced statistical analysis skills and Machine Learning modeling, Deep Learning modeling. Minimum of 3 years of experience with SAS, SQL, Python, SPSS, R or other like applications.
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Python or related language- Experience with neural deep learning methods and machine learningPREFERRED QUALIFICATIONS- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
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Strong experience with data science tools including Python scripting, CUDA, numpy, scipy, matplotlib, scikit-learn, bash scripting and Linux environment. Ability to read and implement related academic literature and experience in applying state-of-the-art deep learning models to computer vision (e.g. segmentation, detection) or other areas as (speech, NLP, GANs.
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Ph. D., M.S. or Bachelors degree in Statistics, Machine Learning, Economics, Computer Science, or other quantitative fields (If M.S. or Bachelors degree, a minimum of 3+ years of industry data science experience required; If PhD degree, a minimum of 1+ years of industry data science experience required.
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Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data.
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FEATURED BLOG POSTS
How to Get a W2 From Previous Employers
When tax time rolls around, the last thing you want to worry about is having to track down a W-2 from your former employer. Many times you won’t have to because the IRS requires companies to send these forms to all current and former employees who have earned more than $600 in the last year. Unfortunately, there are employers who don’t do what they’re supposed to. There are even times where something else may happen that prevents the W-2 from getting where it’s supposed to go.
How to Ask Someone to be a Reference + Email Templates
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: