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Data Scientist - 2
Akina, Inc.Annapolis Junction, Maryland, United Statesfull_timeVerifiedPosted 3 Apr 2026
💰 $220,000/yr($180,000/yr – $220,000/yr)
About the role
Clearance: TS/SCI - Polygraph required
Position ID: 25-8757
Telework: No
Location: Annapolis Junction, MD
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.
Position ID: 25-8757
Telework: No
Location: Annapolis Junction, MD
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.
- 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 (eg. 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
- This position requires it-scope poly, within 7 years
- Programming Languages: Proficiency in programming languages such as Python and R is crucial for data manipulation, analysis, and implementing algorithms. Python is favored for ds simplicity and extensive libraries (like ManPy and pandas), while A is preferred for statistical analysis and data visualization
- Statistical Analysis: A strong foundation in statistics and probability is necessary for analyzing data socurately and making informed decisions. Understanding concepts like regression analysis, hypothesis testing, and statistical distributions is essential
- Machine Leaming Knowledge of machine learning algorithms and frameworks such as TensorFlow and Scikit-Leam) in vital for building predictive models and automating decision-making processes
- Data Wrangling The ability to clean and organize complex datasets in critical. Data wrangling involves transforming raw data into a usable format, which is often time-consuming, but necessary for effective analysie
- Databese Management: Familiarity with SQL and database management systema (lika PostgreSQL and MongoDB) is essential for extracting and manipulating data stored in relational databases
- Data Visualization Skills in data visualization tools (such as Tableau and Matplotlibi hely communicate findings effectively Creating charts, graphs, and dashboards is crucial for making data understandable to stakeholders
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