Data Scientist with Security Clearance

Duties & 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., PostgreSQL, MySQL, Neo4j, RDS).
• Develop and manage ingestion pipelines using tools such as Apache NiFi.
• 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.

Requirements

• 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.

Desired Skills

• Experience with cloud platforms such as AWS and cloud-based data architecture.
• 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.