PSC Flashcards

(3 cards)

1
Q
  • Led development of a desktop application used for training and educational resources for policy analysts using .NET and C#
A

I led the development of a desktop application that provides educational and training resources for our directorate.

Users would open the app and build a profile, and based on the details they provided, would have content provided that is tailored to their profile.

For example, a junior analyst working in a security can see resources for crisis management, and also see milestones needed to advance to a mid-level analyst position.

Technically,

I used WinUI 3 for the front end

I built it with .NET, C#, and SQLite for the back end,

where .NET provided the libraries and framework, and C# was used to implement event handling and logic. A local SQLite database was used to manage training content, user progress, and role-based access.

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2
Q
  • Developed an automated intelligence ingestion and triage system using Python, SQL, and MongoDB, transforming unstructured reports into actionable structured datasets.
A

Analysts were spending too much time manually reviewing intelligence reports, which I was able to identify as a process that could be automated.

This led to me developing automated ingestion and triage system using Python, with MongoDB and SQL.

The system parsed raw reports to extract key fields like locations, threat types, and dates, then applied filtering logic to organize the reports.

This benefit of this was a significantly reduced manual workload and gave analysts faster, more organized access to critical intelligence, improving both efficiency and response times.

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3
Q
  • Developed Python libraries to automate entity scoring and prioritization using rule-based logic and statistical thresholds, reducing analyst workload by over 50%
A

We had a growing stream of intelligence data—press releases, chat logs, events—being ingested continuously into our system called INTELEX.

Each team had different focus areas, making manual triage very time-consuming.

To solve this, I developed a Python library and API that allowed each team to define their own prioritization parameters—for example, by keyword relevance and source.

At a high level, I exposed a config interface where teams could define paramaters, like “if entity_type is ‘organization’ and mention_count > 10, boost the priority score.”

If something deviated significantly—say, an entity showed up 3x more than usual within a sample window—it triggered a boost in score.

The output was structured and routed directly into each team’s internal file system, organized by priority and topic.

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