Z-1360 | Sr. Data Scientist

Required Qualifications: 

• Strong core programming knowledge and    experience.
• Hands on experience building applications using Python.
• Excellent foundation in Machine Learning,Artificial Intelligenceand Statistics/Math.
• Experience with at least one of the following deep learning libraries: 
• TensorFlow 
•  pyTorch.
• Strong experience with the python scientific ecosystem (Pandas, Scikit-learn, Matplotlib, Numpy, Scipy)
• Proficiency with a database query language such as SQL
• Experience or understanding of distributed analytical or big data frameworks such as Spark, H20 or Dask
• Excellent communication (verbal and written) skills.
• Preferred Qualifications:
• Experience with an additional high level      programming language such as Scala or Java.
• Experience with Graph based technologies.
• Hands on experience with image analysis and natural language. Processing (NLP) using advanced Machine Learning techniques such as Convolutional Neural Networks(CNN), Recurrent Neural    Networks(RNN) and word embeddings.

We are looking for experienced data scientists who can help us discover the insights hidden in vast amounts of clinical, biological, environmental, socio-economic, operations and business data. This will help us make smarter applications to deliver better care for our patients and population. Your primary focus will be in applying your data science, engineering and statistical techniques to build high quality analytics, integrated with our delivery of care processes, software systems, and operational processes.

Responsibilities:
The Data Scientist will utilize their skills and competencies in big-data analytics and high performance computing to: 

Build scalable, dynamic and enterprise analytics solutions for healthcare and population health management. 
Develop automated and scalable data quality assurance processes for detecting anomalies, cleansing, and verifying the integrity of data used for analysis.
Develop big data analytics solutions to support large scale inferencing using machine learning techniques.
Improve and extend our existing platforms for machine learning and predictive modelling. 
Participate in developing infrastructure for artificial intelligence (deep learning) and machine learning using all modalities of data (images, free text, streaming data, structured data) using supervised and unsupervised methods.
Develop scalable analytics functions and methodologies for Classification and Regression, Deep Learning, Image Analysis, Natural Language Processing, Bayesian and Probabilistic classifiers and learners, Topological Data Analysis, and more. 
Drive the rigorous data analysis effort and support group efforts toward high quality documentation.
Will work routinely and extensively with data analysis packages such as Pandas and scikit-learn.
Move products through the full development process from research and validation through operational launch.