Data Scientist (Intermediate and Senior)

Who We’re Looking For (Position Overview:)

Spry Methods is seeking Senior and Intermediate Data Scientists to support national security initiatives by delivering actionable data-driven insights. You will collaborate directly with investigators and analysts to understand complex data challenges, apply advanced analytical methods, and deliver results that impact critical missions.

What Your Day-To-Day Looks Like (Position Responsibilities):

• Collaborate with non-technical users to translate mission needs into data science solutions.
• Design and lead machine learning, statistical modeling, and data analysis projects.
• Perform ETL (Extract, Transform, Load) tasks to integrate and condition data from multiple sources.
• Conduct exploratory data analysis and feature engineering; interpret and validate model outputs.
• Innovate new methodologies for processing and analyzing large-scale or complex datasets.
• Develop software to improve data search, handling, and retention capabilities.
• Automate repetitive workflows and analysis procedures to streamline operations.
• Prepare reports and present findings with clear, actionable recommendations.

What You Need to Succeed (Minimum Requirements):

• TS/SCI Clearance
• For Senior-Level Candidates:
• Master’s degree in Data Science, Computer Science, Mathematics, Statistics, or related field.
• High proficiency in Python and a deep understanding of data science tools and methodologies.
• 5+ years of experience in data science or data analytics roles.
• Proven ability to independently lead complex analytical tasks and mentor junior team members.
• For Intermediate-Level Candidates:
• Bachelor’s or Master’s degree in a relevant field.
• Basic to intermediate proficiency in Python and familiarity with data science models.
• 2–4 years of relevant experience supporting data-driven projects.

Ideally, You Also Have (Preferred Qualifications):

• Preferred Experience:
• Experience supporting national security or law enforcement missions.
• Familiarity with big data platforms and tools (e.g., Hadoop, Spark, Elasticsearch).
• Knowledge of data governance, security, and data quality standards in secure environments.

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