Associate Data Scientist

Socket.dev; Norfolk (VA); Hybrid; USD 90,000 - 130,000; Full time

Job summary

FTI is hiring an associate Data Scientist to support the Naval Safety Command in Norfolk, VA. The role involves developing and deploying predictive analytics models to diagnose and predict precursors to Naval mishaps and hazards.

Hybrid work with on-site presence at the Naval Safety Command Center in Norfolk, VA; a DoD Secret Clearance is required to qualify for this position.

Qualifications

• Active 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

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

Skills

Regression analysis

Hypothesis testing

Time-series analysis

Data visualization

Education

Bachelor’s degree in Data Science/Statistics/Math/CS/OR

Tools

Python

R

Pandas

NumPy

Scikit-learn

SciPy

Tableau

Job description

Overview

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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