Data Scientist with Security Clearance

3 weeks ago

Brooklyn Park MD United States Prime Time Consulting Full time
Data Scientist Description: 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. The Data Scientist Skill Level 2 shall possess the following capabilities: * 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, Receiver Operating Characteristic [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 The Data Scientist Skill Level 3 shall possess the following capabilities: * Lead a team of data scientists in the development of multiple analytic efforts * Work with customers and Subject Matter Experts (SMEs) to define analytic requirements and guide the team in formulating analytics that meet requirements * Guide the transition of prototyped analytics to production system * Understand emerging machine learning and pattern recognition algorithms and guide a team of data scientists in integrating state-of-the-art algorithms into solutions * Delegate analysis responsibilities to one or more team members and monitor performance Data Scientist Level 2 Qualifications: Bachelor's and Master's Degree from an accredited college or university in a quantitative discipline (e.g., statistics, mathematics, operations research, engineering or computer science). Five years of experience analyzing datasets and developing analytics, five years of experience programming with data analysis software such as R, Python, SAS, or MATLAB. An additional two years of experience in software development, cloud development, analyzing datasets, or developing descriptive, predictive, and prescriptive analytics can be substituted for a Master's Degree. A PhD from an accredited college or university in a quantitative discipline can be substituted for three years of experience Data Scientist Level 3 Qualifications: * Bachelor's and master's degree or higher from an accredited college or university in a quantitative discipline (e.g., statistics, mathematics, operations research, engineering or computer science). A minimum of ten (10) years of experience in two (2) or more of the following: designing/implementing machine learning, data mining, advanced analytical algorithms, advanced statistical analysis, artificial intelligence, or software engineering with data analysis software such as R, Python, SAS, or MATLAB. * A Master's Degree from an accredited college or university in a quantitative discipline can be substituted for two (2) years of experience for a total of eight (8) years experience required. A PhDfrom an accredited college or university in a quantitative discipline can be substituted for four (4) years of experience for a total of six (6) years experience required. COMPANY PERKS
• 200 hours of PTO annually (All PTO hours available upon start)
• 6% 401k Contribution
• Competitive Health Care Options
• Short Term/Long Term/Life insurance
• Annual Training Budget

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