Data Scientist 3 - (MLOPS, Machine Learning, Data Science)

Akina•Annapolis Junction, MD

5d•Onsite

About The Position

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. Produce data visualizations that provide insight into dataset structure and meaning. Work with subject matters experts (SMEs) to identify important information in raw data and develop scripts that extract this information from a variety of data formats (e.g., SQL tables, structured metadata, network logs). Incorporate SME input into feature vectors suitable for analytic development and testing. Translate customer qualitative analysis process and goals into quantitative formulations that are coded into software prototypes. Develop and implement statistical, machine learning, and heuristic techniques to create descriptive, predictive, and prescriptive analytics. Develop statistical tests to make data-driven recommendations and decisions. 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. Lead a team of data scientists in the development of multiple analytic efforts. Work with customers and 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. Develop machine learning, data mining, statistical and graph-based algorithms to analyze and make sense of datasets.

Requirements

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.
Bachelor's degree or higher from an accredited college or university in a quantitative discipline (e.g., statistics, mathematics, operations research, engineering or computer science).
Clearance: TS/SCI - Polygraph required

Nice To Haves

Significant MLOPS and Data Science background.

Responsibilities

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.
Produce data visualizations that provide insight into dataset structure and meaning.
Work with subject matters experts (SMEs) to identify important information in raw data and develop scripts that extract this information from a variety of data formats (e.g., SQL tables, structured metadata, network logs).
Incorporate SME input into feature vectors suitable for analytic development and testing.
Translate customer qualitative analysis process and goals into quantitative formulations that are coded into software prototypes.
Develop and implement statistical, machine learning, and heuristic techniques to create descriptive, predictive, and prescriptive analytics.
Develop statistical tests to make data-driven recommendations and decisions.
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.
Lead a team of data scientists in the development of multiple analytic efforts.
Work with customers and 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.