Intelligence Data Developer - multiple levels - CLEARANCE and POLYGRAPH REQUIRED

At Constellation Technologies, an Intelligence Data Developer is responsible for analyzing large datasets, developing algorithms, and creating machine learning models that optimize decision-making processes. The role involves leveraging programming languages like Python and Java, as well as databases like MySQL and Oracle to extract meaningful insights and build predictive analytics solutions.

To qualify for the Intelligence Data Developer position at Constellation Technologies, candidates must be U.S. citizens with an active TS/SCI security clearance and polygraph. A Bachelor’s degree in a quantitative field, such as computer science or engineering, is essential, along with one to five years of relevant experience in data analysis and analytics development.

Joining Constellation Technologies as an Intelligence Data Developer comes with a wealth of benefits, such as affordable healthcare options with substantial employer contributions, fully paid dental and life insurance, robust training reimbursement programs, and a generous 401(k) with immediate vesting. The environment is designed to support your professional growth and well-being.

Constellation Technologies is committed to supporting professional development for Intelligence Data Developers. This includes annual training and education reimbursement, unlimited access to various training resources including AWS and Red Hat Enterprise Linux, and ample opportunities for mentorship and leadership engagement.

Intelligence Data Developers at Constellation Technologies will work on a range of projects that involve developing experiments to collect data, evaluating the performance of analytics using standard metrics, and tuning machine learning algorithms to achieve optimal results. The focus is on delivering key insights from diverse data streams that impact military and intelligence operations.

When answering this question, highlight specific algorithms you've used in previous projects and how they contributed to the analysis of data sets. Discuss your understanding of algorithm selection and tuning based on data characteristics.

Make sure to list the programming languages you’re most comfortable with, such as Python and R, and provide examples of projects where you successfully utilized these languages to solve data challenges.

Use this opportunity to walk the interviewer through a specific project, detailing the datasets you analyzed, the tools and methods you employed, and the outcomes that resulted from your analysis. Focus on problem-solving and innovation.

Discuss various validation techniques you apply, such as cross-validation, ROC curves, or confusion matrices. Emphasize your systematic approach to ensure the reliability of your models.

Talk about your familiarity with cloud platforms like AWS or specific database systems like MySQL or Oracle, providing examples of how you've utilized these technologies in past projects.

Select a situation where you translated complex data insights into understandable terms for a non-technical audience. Highlight your communication skills and ability to bridge the gap between technical detail and practical implications.

Answer by discussing your process for identifying relevant features, creating new variables, and the importance of feature selection in improving model performance. Share specific examples where appropriate.

Discuss different analytics methodologies you've employed, such as descriptive, predictive, or prescriptive analytics, and how you determine which approach is most suited for a given project.

Mention any statistical software you have experience with, such as SAS or MATLAB. Provide examples of how you've applied these tools to perform analyses or develop insights.

Reflect on your passion for data and analytics, your desire to solve complex problems, and how contributing to vital projects aligns with your personal and professional aspirations. Share how this motivation drives your work ethic.