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Acquisition Data Scientist / Affordability Analyst - Johns Hopkins University Applied Physics Laboratory
Data Scientist
Data scientist role focused on building analytic frameworks for acquisition cost and schedule management, leveraging Python, R, SQL, machine learning, and visualization to drive data‑driven resource allocation decisions.
About the role
Key Responsibilities
- Design and implement advanced analytical models to forecast project cost, schedule performance, and affordability metrics.
- Integrate disparate data sources—financial, engineering, and logistics—into structured, queryable datasets.
- Develop machine‑learning pipelines and statistical tools to identify risk drivers and recommend mitigation strategies.
- Create interactive dashboards and visualizations to communicate insights to program managers and senior leadership.
- Collaborate with acquisition, engineering, and finance teams to translate analytical findings into actionable acquisition decisions.
Requirements
- Master’s degree in Data Science, Applied Mathematics, Engineering, or a related quantitative field.
- Proficiency in Python and R for data manipulation, statistical analysis, and machine‑learning model development.
- Strong SQL skills for data extraction, transformation, and loading from large relational databases.
- Experience building predictive models, time‑series forecasts, and optimization algorithms for cost‑schedule analysis.
- Ability to produce clear, data‑driven visualizations and reports for non‑technical stakeholders.
Skills
pythonsqlmachine learning