Data Scientist Security Clearance

Responsibilities

• Design, build, and maintain scalable data pipelines and infrastructure supporting analytics, reporting, and machine learning use cases
• Develop and optimize ETL and ELT workflows for structured and unstructured data sources
• Build and maintain data integration layers across cloud, web, and on‑premise systems
• Ensure data quality, consistency, security, and reliability across data pipelines and storage systems
• Develop data processing solutions using Python, SQL, and Bash scripting in Linux environments
• Construct and optimize complex queries across multiple data sources (e.g., Postgre
  
  SQL, MySQL, Neo4j, RDS)
• Develop and manage ingestion pipelines using tools such as Apache Ni Fi
• Process and transform large-scale datasets from diverse structured and unstructured sources
• Develop reusable, tested, and reproducible data workflows and Python‑based modules
• Use Elasticsearch and Kibana for search, indexing, and data visualization use cases
• Document technical solutions, data pipelines, and methodologies for both technical and non‑technical stakeholders
• Communicate findings through written reports, dashboards, and oral briefings to stakeholders
• Collaborate across multiple teams to support data‑driven decision‑making and analytics initiatives
• Support knowledge sharing by explaining complex data concepts to junior team members

• Strong experience in data engineering and data pipeline development, including ETL/ELT design and implementation
• Proficiency in Python programming for data processing and automation
• Strong experience with SQL and relational database systems
• Experience working in Linux environments with advanced Bash scripting
• Experience building and managing data pipelines using Apache NiFi or similar tools
• Experience processing both structured and unstructured data sources
• Experience working with Elasticsearch and Kibana
• Experience using Git-based version control systems
• Experience using Jupyter Notebooks for analysis and prototyping
• Experience delivering technical results through documentation and stakeholder briefings
• Strong Communication Skills And Experience Working With Multiple Stakeholders
• Experience creating reusable, tested, and maintainable data solutions
• Academic or professional background in math, statistics, physics, computer science, data science, or related fields

• Experience with cloud platforms such as AWS and cloud‑based data architectures
• Experience with big data processing frameworks such as Apache Spark or Trino
• Experience applying machine learning algorithms and NLP techniques
• Experience with containerization technologies such as Docker or Kubernetes
• Experience with data visualization tools such as Tableau, Kibana, or Apache Superset
• Experience working with or designing machine learning workflows and models
• Experience creating training materials or technical curriculum in data or scientific domains
• Familiarity with data science MLOps or production ML workflows

• Flexible time off
• Full medical coverage
• 401(k) with company match
• Referral bonuses
• Performance bonuses
• Life insurance and disability coverage
• Tuition and training reimbursement