Data Scientist

Quest Defense Systems & Solutions•Cincinnati, OH

15h

About The Position

Quest Defense is seeking a highly motivated Data Scientist to support data-driven program execution, operational reporting, engineering analytics, and decision support for safety and mission-critical aerospace programs. The Data Scientist will work with program managers, engineers, technical teams, customer stakeholders, and business leaders to collect, structure, transform, analyze, visualize, and communicate data that supports delivery performance, resource planning, schedule execution, financial tracking, operational improvement, and engineering decision-making. This role requires someone who can operate in a modern data environment, work through imperfect and disconnected data sources, build repeatable data pipelines, improve data hygiene, and translate complex information into clear insights, dashboards, reports, and recommendations. The ideal candidate has strong Python skills, experience with Dataiku or similar data platforms, exposure to AWS or comparable cloud environments, and the ability to present data in a way that helps leaders and technical teams make better decisions.

Requirements

Bachelor’s degree in Data Science, Computer Science, Engineering, Statistics, Mathematics, Information Systems, Business Analytics, Data Analytics, or a related field; equivalent experience may be considered.
2+ years of hands-on experience in data science, data analytics, data engineering, business intelligence, program analytics, operations analytics, or a related role.
Strong Python skills, including experience with data manipulation, transformation, analysis, and automation.
Experience with SQL and working with relational or structured datasets.
Experience building reports, dashboards, or visualizations using tools of any sort.
Experience cleaning, joining, transforming, and validating data from multiple sources.
Understanding of data pipelines, ETL/ELT concepts, and repeatable data workflows.
Ability to analyze data, identify trends, communicate findings, and translate business questions into analytical outputs.
Strong attention to detail and commitment to data accuracy, data quality, and data hygiene.
Ability to work with cross-functional technical and non-technical teams.
Strong written and verbal communication skills.
Ability to manage multiple priorities in a fast-paced program or engineering environment.

Nice To Haves

Experience with Dataiku or similar data science, analytics, or workflow automation platforms.
Experience with AWS data services or cloud-based analytics environments.
Experience with PostgreSQL, Redshift, or similar data platforms.
Experience developing, maintaining, or optimizing data pipelines.
Experience with machine learning models, statistical analysis, predictive analytics, anomaly detection, or forecasting.
Exposure to natural language processing, AI/LLM integration, or agentic AI workflows.
Experience supporting aerospace, defense, manufacturing, engineering, supply chain, quality, or program management environments.
Familiarity with program management metrics, schedule tracking, cost tracking, utilization, staffing, risk management, operational KPIs, or engineering workflow analytics.
Experience working with customer-facing teams or supporting executive-level reporting.
Ability to create repeatable reporting processes and improve data quality over time.
Experience with data storytelling, visual management, and executive-ready presentation of insights.
Familiarity with controlled data environments, customer systems, export-controlled information, or compliance-sensitive reporting.
Experience working within Lean, Six Sigma, Agile, or continuous improvement environments.

Responsibilities

Collect, clean, organize, transform, and analyze data from multiple business, engineering, operational, and program sources.
Build, maintain, and improve data pipelines that support repeatable reporting, analytics, dashboards, and decision-support products.
Work in Dataiku or similar data science / data engineering platforms to prepare datasets, develop workflows, automate analysis, and support scalable analytics.
Use Python to clean, transform, analyze, and automate data processes.
Support data integration, data transformation, and data hygiene across structured, semi-structured, and manually maintained data sources.
Develop dashboards, trackers, reports, and visualizations that support program execution, customer reviews, operating reviews, and leadership visibility.
Analyze trends, gaps, risks, anomalies, and performance indicators related to schedule, cost, staffing, utilization, delivery, engineering workflows, and operational performance.
Partner with program managers, delivery leaders, engineers, supply chain, quality, and customer stakeholders to understand data needs and define useful reporting outputs.
Validate data accuracy, identify inconsistencies, and recommend improvements to source data, reporting logic, and business processes.
Support development of repeatable analytics processes, templates, standard work, data models, and documentation.
Assist with ad hoc analysis, scenario planning, forecasting, executive reporting, and root cause analysis.
Communicate findings clearly through concise visualizations, summaries, recommendations, and executive-ready presentations.
Help improve data discipline, reporting cadence, data governance, and visibility across programs.
Contribute to the development of advanced analytics, automation, predictive models, and AI-enabled workflows where appropriate.