My client is looking for a Data Scientist. This position is remote. The duration of this project is 6 months.
Skills Required :
EXPERIENCE AND EDUCATIONAL REQUIREMENTS:
- Experience with large datasets in distributed environments
- Strong statistical application experience (application in healthcare preferred)
- Good experience in utilizing Data using SQL and other data pipelines
- Machine Learning, NLP experience
- Hands-on with Python, SAS, or R analytics tools/language
- Healthcare background preferred ( Molecular Microbiology, Pathobiology, BioInformatics, Molecular Pharmacology etc.)
Requires training in fields such as engineering, statistics, mathematics, economics or similar vocations generally obtained through completion of a four-year bachelor’s degree. Strong problem-solving and analytical skills with experience in statistical modeling is preferred. Proficient with Excel, Python/R , SAS and Databricks preferred. Requires a minimum of one (1) to two (2) years’ experience as a Data Scientist.
MINIMUM SKILLS, KNOWLEDGE, AND ABILITY REQUIREMENTS:
- Strong analytical, problem solving, and modeling skills to evaluate business problems and apply applications knowledge to identify appropriate solutions
- Proficient in SAS or Python or R data analytics tools as well as Microsoft SQL query tools.
- Understanding of Machine Learning operations management or experience with Databricks MLflow.
- Designs experiments, test hypotheses and builds analytic models.
- Deep knowledge of NLP text mining especially information extraction from clinical data
- Deep knowledge of healthcare informatics and/or information management
- Experience working with domain ontologies such as ICD9 & 10, MeSH, MedRA, RxNorm, SNOMED CT
- Familiarity with healthcare and life science information (e.g. EHRs)
- Conducts data analysis and designs and develops moderately complex analytic algorithms.
- Validates analysis by comparing appropriate samples.
- Employs the appropriate algorithm to discover patterns.
- Experience with state-of-the-art techniques in machine learning algorithms, including deep neural networks, NLP, dimensionality reduction, ensemble methods, graph algorithms