Job Description
Clearance Required: TS/SCI with Poly
Job Summary
Elder Research Inc. is a Data Science consulting firm specialized in providing analytic solutions to clients in Commercial and Government industries. Providing analytic solutions to hundreds of companies across numerous industries, our team enjoys a great variety in the type of work they do and exposure to a wide range of techniques and tools.
We are trusted advisors to our clients, building lasting relationships and partnering as preferred analytics providers. We use a variety of programming languages and tools to create analytic solutions, often fitting within our clients’ environment and needs.
Join our team and find great opportunities to hone your analytic skills, work on complex problems with amazing teammates, and gain valuable analytics consulting experience.
Position Responsibilities:
A data scientist will develop machine learning, data mining, statistical and graph-based algorithms to analyze and make sense of datasets; prototype or consider several algorithms and decide upon final model based on suitable performance metrics; build models or develop experiments to generate data when training or example datasets are unavailable; generate reports and visualizations that summarize datasets and provide data-driven insights to customers; partner with subject matter experts to translate manual data analysis into automated analytics; implement prototype algorithms within production frameworks for integration into analyst workflows.
Required Degree and Experience:
Bachelor's degree in a quantitative discipline (e.g., statistics, mathematics, operations research, engineering or computer science) + 7 years of experience, Masters Degree + 5 years of experience, or PhD + 2 years of experience
Required Skills:
Programming experience with data analysis software such as R, Python, SAS, or MATLAB.
Develop experiments to collect data or models to simulate data when required data are unavailable.
Develop feature vectors for input into machine learning algorithms.
Identify the most appropriate algorithm for a given dataset and tune input and model parameters.
Evaluate and validate the performance of analytics using standard techniques and metrics (e.g. cross validation, ROC curves, confusion matrices).
Oversee the development of individual analytic efforts and guide team in analytic development process.
Guide analytic development toward solutions that can scale to large datasets.
Partner with software engineers and cloud developers to develop production analytics.
Develop and train machine learning systems based on statistical analysis of data characteristics to support mission automation.
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