The Health and Safety Solutions Operation of Leidos is seeking a Data Scientist in Bethesda, MD
- Provide artificial intelligence and data analytics support to NIH Assisted Referral Tool (ART)
- Develop a systemic classification system to characterize the science reviewed in NIH SRGs and SEPs
- Provide continued development of a platform that supports continuous evolution for increased accuracy and robustness of the recommendation.
- Develop and Standardized SQL scripts
- Develop a robust data validation methodology
- Curated dataset for analyses and upgrade
- Establish deviation standard and upgrade the scripts for accuracy
- Analyze variance and upgrade the software
Required Educational Qualifications:
- B.S. in computer science or equivalent. Master's degree in Bioinformatics or data science preferred.
- Minimum 7 years of relevant experience.
- Experience with programming languages such as R, Python, Pearl, C++
- Demonstrated experience in handling large data sets, performing predictive analytics such as machine learning and data mining.
- Experience with the application of bioinformatics and statistical tools and the analysis
- Has the ability to utilize advance tools and computational skills to interpret, connect, predict and make discoveries in complex data and deliver recommendations for business and analytic decisions.
- Experience with handling large data sets, generate predictive regression models (logistic and multivariate) and apply machine learning algorithm such as random forest, elastic net.
- Experience with developing machine learning algorithms for object detection and recognition, large-scale indexing and retrieval and multimedia understanding
- Excellent presentation and communication skills; Demonstrated ability to clearly explain technical issues to technical and non-technical customers
- Experience with Cloud computing environments such as AWS, Azure.
- Experience with various bioinformatics databases and tools.
- Previous experience working with NIH
- Strong knowledge of NIH grants management processes and systems
- Knowledge of NIH peer review process