Sr Machine Learning Engineer Data Scientist - Hybrid Day 1

Sr. Machine Learning Engineer Data Scientist Hybrid Day 1

Job Summary

Our client is growing and the data processing landscape is shifting. We collect terabytes of data of various types including realtime from a cadre of sources from all of our clients. Our clients large and small health systems too are growing. The kinds and amount of data that each generates on a yearly basis is exponentially growing. Having this data improves our clients situational awareness leading them to better understand how they are performing in order to improve their overall outcomes. This is true for the smallest community hospital to the largest health system.

As a Data Scientist you will be working in one of the most complex data environments in healthcare. We are developing stateoftheart largescale Machine Learning Services and Applications on the Cloud involving large amounts of data. We work on applying predictive technology to a wide spectrum of problems. We are looking for talented and experienced Machine Learning Engineers/Data Scientists who can apply innovative Machine Learning techniques to realworld problems. You will get to work in a team dedicated to advancing Machine Learning solutions and converting them into operational improvements for healthcare. You should be someone who loves to bring data together to answer business questions and drive change.

Experience

Our client has a large and robust healthcare data set. We are expanding our team to include experienced machine learning engineers to help create actionable insight from this data. You will work with data experts to integrate predictive analytics into the healthcare operations.

• Experience with applying machine learning models to solve realworld problems (i.e. with multiple stake holders) as part of their onthejob duties.
  
• Emphasis on analysis and model development but experience deploying a model for an endtoend app solution of some sort.
  
• Advanced machine learning skills using Python or R: clustering regression classification and machine learning frameworks such as Apache Mahout.
  
• Assess model performance; train multiple models; carry out tuning. Run A/B tests on models.
  
• Some familiarity with deep learning.
  
• Experience working with database systems.
  
• Transform and clean data into an appropriate form for statistical analysis.
  
• Work with domain experts to identify areas where predictive analytics can take us to the next level.
  
• Be an active listener probe requirements for all projects from relevant stakeholders stay nimble and willing to produce rapid iterations.
  
• Be capable of selfdirection within context.
  

Skills:

• Some familiarity with deep learning.
  
• 36 years of machine learning experience. Experience using predictive analytics in a business or healthcare setting.
  
• Deep knowledge of a scripting or statistical programming language (Python preferred). Basic SQL. Ability to efficiently work with very large datasets. Ability to deal with nonstandard machine learning datasets (classimbalances sparse matrices etc.)
  
• Comfortable writing complex SQL queries. Experience developing python packages. Version control experience in Git or a similar system. Experience with agile development practices. Experience with Hadoop. Experience with lowlevel machine learning libraries
  
• 36 years of developing implementing machine learning algorithms using Python.
  
• 510 years of SQL
  
• 2 years working with opensource Big Data technology stacks (Apache Nifi Spark Kafka HBase Hadoop/HDFS Hive Drill Pig etc.) or commercial open source Big Data technology stacks (Hortonworks Clouder etc.)
  
• 310 years with document databases (e.g. MongoDB Accumulo etc.)
  
• 25 years of distributed version control system (e.g. git)
  
• 15 years of experience in cloudbased development and delivery
  

Duties and Responsibilities

• Use machine learning data mining and statistical techniques to create new scalable solutions for business problems.
  
• Analyst and extract relevant information from business data to help automate and optimize key processes.
  
• Design develop and evaluate highly innovative models for predictive learning.
  
• Establish scalable efficient automated processes for large scale data analyses model development model validation and model implementation.
  
• Research and implement novel machine learning and statistical approaches.
  

Education:

• Master s Degree in Computer Science or other related field of study (preferred).
  
• Bachelors Degree in Computer Science or other related field of study (required).
  

Sr. Machine Learning Engineer Data Scientist Job Summary Our client is growing and the data processing landscape is shifting. We collect terabytes of data of various types, including real-time, from a cadre of sources from all of our clients. Our clients, large and small health systems, too are growing. The kinds and amount of data that each generates on a yearly basis is exponentially growing. Having this data improves our clients situational awareness, leading them to better understand how they are performing in order to improve their overall outcomes. This is true for the smallest community hospital to the largest health system. As a Data Scientist you will be working in one of the most complex data environments in healthcare. We are developing state-of-the-art large-scale Machine Learning Services and Applications on the Cloud involving large amounts of data. We work on applying predictive technology to a wide spectrum of problems. We are looking for talented and experienced Machine Learning Engineers/Data Scientists who can apply innovative Machine Learning techniques to real-world problems. You will get to work in a team dedicated to advancing Machine Learning solutions and converting them into operational improvements for healthcare. You should be someone who loves to bring data together to answer business questions and drive change. Experience Our client has a large and robust healthcare data set. We are expanding our team to include experienced machine learning engineers to help create actionable insight from this data. You will work with data experts to integrate predictive analytics into the healthcare operations. Experience with applying machine learning models to solve real-world problems (i.e. with multiple stake holders) as part of their on-the-job duties. Emphasis on analysis and model development, but experience deploying a model for an end-to-end app solution of some sort. Advanced machine learning skills using Python or R: clustering, regression, classification, and machine learning frameworks such as Apache Mahout. Assess model performance; train multiple models; carry out tuning. Run A/B tests on models. Some familiarity with deep learning. Experience working with database systems. Transform and clean data into an appropriate form for statistical analysis. Work with domain experts to identify areas where predictive analytics can take us to the next level. Be an active listener, probe requirements for all projects from relevant stakeholders, stay nimble and willing to produce rapid iterations. Be capable of self-direction, within context. Skills: Some familiarity with deep learning. 3+ years of machine learning experience. Experience using predictive analytics in a business or healthcare setting. Deep knowledge of a scripting or statistical programming language (Python preferred). Basic SQL. Ability to efficiently work with very large datasets. Ability to deal with non-standard machine learning datasets (class-imbalances, sparse matrices, etc.) Comfortable writing complex SQL queries. Experience developing python packages. Version control experience in Git or a similar system. Experience with agile development practices. Experience with Hadoop. Experience with low-level machine learning libraries 3+ years of developing implementing machine learning algorithms using Python. 5+ years of SQL 2+ years working with open-source Big Data technology stacks (Apache Nifi, Spark, Kafka, HBase, Hadoop/HDFS, Hive, Drill, Pig, etc.) or commercial open source Big Data technology stacks (Hortonworks, Clouder, etc.) 3+ years with document databases (e.g. MongoDB, Accumulo, etc.) 2+ years of distributed version control system (e.g. git) 1+ years of experience in cloud-based development and delivery Duties and Responsibilities Use machine learning, data mining, and statistical techniques to create new, scalable solutions for business problems. Analyst and extract relevant information from business data to help automate and optimize key processes. Design, develop, and evaluate highly innovative models for predictive learning. Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation, and model implementation. Research and implement novel machine learning and statistical approaches. Education: Master s Degree in Computer Science or other related field of study (preferred). Bachelor's Degree in Computer Science or other related field of study (required)