FTI is hiring an associate Data Scientist to support the Naval Safety Command in Norfolk, VA. As a member of the data science team, you will be working with a team of Data Scientists and Software Engineers to support the development, testing, and deployment of a series of advanced predictive analytics models using data sets that will help diagnose and predict precursors to Naval mishaps and safety hazards.
This is a hybrid position with an on-site at the Naval Safety Command Center in Norfolk, VA. A DoD Secret Clearance is required for this position.
Responsibilities
• Support in the designing, calibrating, and testing of a portfolio of predictive risk models to evaluate mishap risk for individual Navy communities.
• Support analytical focus on extracting insights from data to make predictions, understand relations, and identify unusual patterns using approaches like time‑series/forecasting, causal inference, statistical modeling, and anomaly detection
• Support feature engineering, cross-validation, and creation of performance metrics (precision, recall) to minimize error and eliminate overfitting.
• Partner with software engineers and senior data scientists to integrate features and transition analytical models into operational environments.
• Participate in technical exchange meetings and assist in training personnel on model maintenance and interpretation.
Education/Qualifications
Required:
• Active Department of Defense (DoD) Secret Clearance
• Bachelor's Degree in Data Science, Statistics, Mathematics, Computer Science, Operations Research, or a related field.
• 1-2 years of practical data science/analytics experience (or a Master’s degree with substantive applied research/project experience).
• Proficiency in Python or R, or a similar language
• Practical experience with analytical and machine learning toolkits, such as Pandas, NumPy, Scikit-learn, SciPy, or related packages.
• Foundational understanding of regression analysis, probability distributions, hypothesis testing, and simulation or Bayesian modeling techniques.
Preferred:
• Ability to develop data visualizations and functional dashboards in Qlik, Tableau, or Python-based visualization packages.
• Exposure to Databricks or Apache Spark
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