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Data Scientist
1 month ago
ID: 499818
Type: Researchers
Location: Atlanta, GA
Categories: Artificial Intelligence, Data Analytics/Science, Health Informatics
Overview:
The Georgia Tech Research Institute (GTRI) is the nonprofit, applied research division of the Georgia Institute of Technology (Georgia Tech). Founded in 1934 as the Engineering Experiment Station, GTRI has grown to more than 2,900 employees, supporting eight laboratories in over 20 locations around the country and performing more than $940 million of problem-solving research annually for government and industry. GTRI's renowned researchers combine science, engineering, economics, policy, and technical expertise to solve complex problems for the U.S. federal government, state, and industry.
Georgia Tech's Mission and Values
Georgia Tech's mission is to develop leaders who advance technology and improve the human condition. The Institute has nine key values that are foundational to everything we do:
1. Students are our top priority.
2. We strive for excellence.
3. We thrive on diversity.
4. We celebrate collaboration.
5. We champion innovation.
6. We safeguard freedom of inquiry and expression.
7. We nurture the wellbeing of our community.
8. We act ethically.
9. We are responsible stewards.
Over the next decade, Georgia Tech will become an example of inclusive innovation, a leading technological research university of unmatched scale, relentlessly committed to serving the public good; breaking new ground in addressing the biggest local, national, and global challenges and opportunities of our time; making technology broadly accessible; and developing exceptional, principled leaders from all backgrounds ready to produce novel ideas and create solutions with real human impact.
Project/Unit Description
The Health Emerging & Advanced Technologies (HEAT) Division is dedicated to improving individual and population health through advances in the capture, analysis, and standards-based, interoperable communication of health data. Our goals are 1) to advance foundational technologies in health data analytics; 2) to promote interoperability and open-source platforms in health; 3) to support healthcare partners in maximizing the value of their data resources; and 4) to foster scalable and secure health research locally and globally.
The Health Analytics and Phenotyping Branch (HAPB) in the HEAT Division is dedicated to solving applied research problems in public health, both nationally and specifically in the state of Georgia. Data scientists in HAPB employ data science solutions to improve health outcomes and inform policy decisions. This branch focuses on leveraging big data, data visualization, artificial intelligence, and statistical methods to 1) identify and analyze health trends, 2) support health decision making, 3) enhancing disease surveillance, and 4) promote health equity.
Job Purpose
The Data Scientist interprets and analyzes data to solve problems and sponsor needs, utilizing advanced algorithms in statistics, machine learning, and artificial intelligence for analyzing and classifying datasets. In addition, the Data Scientist extracts hidden information from large sources of raw data to deliver value to business stakeholders and applies state-of-the-art data mining techniques, conducts statistical analyses, builds high-quality prediction models, and data visualizations. The Data Scientist is also responsible for selecting features as well as building and optimizing classifiers using machine learning or statistical techniques. The Data Scientist creates a thorough plan for model validation and conducts A/B testing when required. This position requires a strong educational background in computer science and/or mathematics, particularly with an advanced degree. Additionally, the Data Scientist may perform physics-based modeling, applied quantum algorithms, and artificial intelligence (AI).
Key Responsibilities
- Customize complex models and algorithms using state-of-the-art tools and frameworks
- Collaborate with sponsors to determine actionable approaches to modeling
- Perform model validation and A/B testing
- Conduct data analysis using tools such as R, Python, Julia, SAS, SPSS, and/or Tableau
- Make a meaningful impact on health research by preparing data, analyzing longitudinal trends, performing statistical analyses, building data visualizations, and communicating findings. This includes research projects utilizing claims, observational, demographic, survey, and other types of health data.
- Uncover important trends from data by integrating data from multiple sources, ensuring data quality, and managing metadata. Utilize linked data sets to better understand clinical findings, longitudinal trends, geospatial factors, health disparities, and demographic trends.
- Engage in health research by exploring literature in computer science, statistics, and mathematics to find innovative solutions for real-world health research problems. Develop and apply novel solutions to address health research challenges.
- Support fellow researchers by mentoring students, junior data scientists, and public health researchers. Collaborate on multidisciplinary research, and review work for quality improvement. Communicate verbal and written feedback in a clear and effective manner.
- Strong proficiency in Python and SQL.
- Strong statistics, computer science, or data analytics background.
- Experience or strong interest in analyzing health data, including but not limited to claims data, electronic medical record data, and public health survey data.
- Experience accessing and analyzing diverse data sets (e.g. flat files, APIs, and relational databases)
- Experience in performing analytical tasks with data including statistical methods and data visualization.
- Experience communicating and presenting data findings, both written and verbal.
- Experience mentoring or teaching data science concepts and communicating technical concepts to lay audiences.
- Strong experience with Python libraries, including but not limited to pandas, numpy, matplotlib, etc.
- Experience with R and Shiny.
- Experience working with sensitive health care data (PHI/PII) and secure environments.
- Experience working with the OMOP Common Data Model / OHDSI tools.
- Experience working in cloud environments, such as Databricks.
- Experience building data visualizations with Tableau and Power BI.
- Experience using analytical skills to perform population-level estimation and patient-level prediction.
- Experience with natural language processing and large language models.
- Experience with building, running, and, tuning machine learning models.
- Experience gathering and cleaning data from the web via APIs and other web scraping techniques.
- Experience training and developing educational materials on topics around health data analytics.
- Applicants should have a Master's Degree or Ph.D. in Data Science, Data Analytics, Statistics, Computer Science, or a related field, with a minimum of 3 years of professional work experience.
- Applicants should have a Master's Degree or Ph.D. in Data Science, Data Analytics, Statistics, Computer Science, or a related field, with a minimum of 3 years of professional work experience.
