Associate Data Scientist

at  Iodine Software

WHO WE ARE:

Iodine is an enterprise AI company that is championing a radical rethink of how to create value for healthcare professionals, leaders, and their organizations: automating complex clinical tasks, generating insights and empowering intelligent care. Powered by one of the largest sets of clinical data and use cases available, our groundbreaking clinical machine-learning engine, Cognitive ML, constantly ingests the patient record to generate real-time, highly focused, predictive insights that clinicians and hospital administrators can leverage to dramatically augment the management of care delivery.

WHAT WE’RE LOOKING FOR:

Iodine thrives on innovation. We are looking for a curious, passionate Data Scientist that will help us discover and unlock the value hidden in vast amounts of data. You will be joining a small team that is tackling many challenging and interesting healthcare problems using ML and NLP. Because we work on the cutting edge of a lot of technologies, we need someone who is a creative problem solver, resourceful in getting things done, and productive working independently or collaboratively. You will be accessing and shaping our enormous amount of data to help drive our future innovation.

DESIRED ADDITIONAL EXPERIENCE/SKILLS:

• Familiarity with big data and distributed/cloud computing technologies such as Airflow, Kafka, AWS platform data services
• Experience with noSQL databases including ElasticSearch
• Knowledge of healthcare data space, including clinical data formats and workflows is strongly preferred
• Very strong scripting and automation skills – you believe you can automate everything)
• Understand database platform layouts and configurations
  Please note this position is not restricted solely to the responsibilities listed above and that the job scope and responsibilities are subject to change.

• Work with a team of passionate data scientists to further the mission of Iodine and the Data Science team
• Collaborate with teams, including both technical and business teams, from different functional areas to solve complex problems
• Develop statistical and machine learning algorithms, named entity recognition/named entity linking models, inference rules, probabilistic models, simulation models, and visualizations for client pilots and scaled solutions
• Assist other team members as required
• Consult with business teams to provide guidance and capture feedback as required
• Perform exploratory data analysis (EDA), data profiling, and data cleaning on a variety of datasets
• Learning and identifying data relationships across multiple contributing sources, developing and discovering direct and inferred data relationships where rules can be defined and consistently applied, and other strategies to optimize the efficiency and quality of the data being collected, normalized, and delivered
• Acquire data from primary or secondary data sources to maintain data quality programs, and leverage these sources to identify and interpret trends or patterns in complex datasets