Data Scientist (NLP - Natural Language Processing)

Indianapolis, IN, United States

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Company Description

*Our online application process only takes a few minutes.*

Who is Authenticx?

Authenticx is on a mission to help humans understand humans. Our platform is the new standard for humanizing customer interaction data at scale. We do this by channeling our passion and talent into helping health care leaders listen to their conversational data in a way that delivers value to the enterprise.

What we offer our team members?

  • A culture based on our core values of Authenticity, Courage and Having Fun
  • A collaborative environment that supports your personal and professional development
  • Virtual/remote working
  • Health insurance effective on DAY 1, including FREE health insurance options and a comprehensive benefits package
  • Health Savings Account (HSA) and Flexible Spending Account (FSA) options
  • 401(k) - Traditional and Roth options
  • Unlimited vacation time, plus paid sick time, holiday pay and parental leave
  • Perks at Work membership for discounts on shopping, travel and much more

Job Description

As a Data Scientists at Authenticx, you will:

  • Develop machine learning (ML) models using natural language processing (NLP) that will be integrated into our automated processes
  • Serve as a technical leader and early adopter of technology while developing new features with varying levels of design specs
  • Troubleshoot and resolve issues and provide technical solutions
  • Utilizie Python and machine learning (ML) technologies to perform conversational analytics
  • Analyze structured and unstructured data and build predictive models using a variety of approaches
  • Search through large data sets and transform data to make it more appropriate for analysis  
  • Create reports and presentations for business uses  

Qualifications

As our ideal Data Scientist, you will possess the following skills & qualifications:

Basic Qualifications: 

  • B.S. in Mathematics, Economics, Computer Science, Statistics, or another quantitative field 
  • 2+ years of experience as a Data Scientist, Machine Learning Engineer, or relevant school work. 
  • Strong knowledge of Natural Language Processing (NLP) or Audio Signal Processing 
  • Proficiency with Python (Pandas, Numpy, Matplotlib, Scikit-Learn, Spacy, Scipy, etc.) 
  • Experience with a deep learning framework (Tensorflow, Pytorch, etc.) and deep learning architectures (RNNs, CNNs, Transformers, etc.) 
  • Strong mathematics skills (e.g., Linear Algebra, Statistics)  
  • Advanced ability to perform exploratory data analysis and to communicate complex data in a simple, actionable way  
  • Experience with SQL, databases, and other data management tools  
  • Ability to work independently and with team members from different backgrounds  
  • Excellent attention to detail 
  • Comfortable interacting with executive level leaders  

Preferred Qualifications: 

  • Software-as-a-service (SaaS) and/or healthcare industry experience 
  • Experience with cloud technologies a plus (Azure, AWS, GCP) 
  • Experience with the full life cycle of model development. From data acquisition to training and deploying the model in a production environment.  
  • Exceptional technical writing skills  

Additional Information

  • This is a remote/virtual position.
  • You must live in the United States of America. 
  • You must be authorized to work in the USA, now and in the future, without requirement of sponsorship/visa.

* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰

Tags: Architecture AWS Azure Computer Science Data analysis Data management Deep Learning Economics EDA GCP Linear algebra Machine Learning Mathematics Matplotlib ML models NLP NumPy Pandas Python PyTorch Scikit-learn SciPy spaCy SQL Statistics TensorFlow Transformers Unstructured data

Perks/benefits: Career development Flex hours Flexible spending account Flex vacation Health care Parental leave Unlimited paid time off

Region: North America
Country: United States
Job stats:  22  3  0

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