Cleared Data Scientist (Multiple Levels)

Responsibilities

• Working with open-source data and statistical software packages, cloud services, and AI/ML technologies to find patterns and relationships in large volumes of data.
• Analyzing and interpreting large, complex datasets.
• Exploring and modeling data to uncover patterns and features of interest.
• Designing, building, and training machine learning models.
• Collecting, curating, and processing large-scale structured and unstructured datasets.
• Researching, implementing, and documenting analytical approaches and outcomes.
• Utilizing high-performance computing resources, including GPU clusters and cloud-based platforms.
• Applying data visualization (e.g., Tableau) to communicate findings to stakeholders.

Required Qualifications

• Active Top-Secret SCI (TS/SCI) with Polygraph.
• Experience with machine learning, statistical modeling, and/or time-series forecasting.
• Proficiency in data science tools and technologies such as Python, SQL/PostgreSQL, Spark, and Git (NiFi is a plus).
• Experience working with cloud platforms such as AWS, Google Cloud Platform (GCP), or Microsoft Azure.
• Experience working with Cyber data to include examples such as Shodan and Censys.IO.
• Experience with data visualization tools (e.g., Tableau).
• Experience working with open-source datasets.
• Demonstrated ability to clean, manage, and optimize large-scale datasets.
• Experience analyzing data in Python.
• Fundamental understanding of a range of AI techniques and ability to match techniques to problems.
• Demonstrated ability to effectively communicate technical concepts to a variety of audiences verbally and in writing.
• US Citizenship is required.

Desired Qualifications

• Familiarity with developing, retraining, or using AI and Machine Learning packages.
• Knowledge of software and hardware optimization techniques for large-scale data processing.
• Proficient in data warehousing, data management, and ETL tools (e.g., Apache NiFi, Pentaho, Kafka).
• Advanced degree in a data science equivalent field or sub-field.