From FRONTIER TECHNOLOGY’s careers page.
Overview
FTI is seeking an early career Data Scientist to support the Naval Safety Command in Norfolk, VA. This is an excellent opportunity for a recent Master’s graduate or current PhD student who wants to apply their academic experience to real world AI, machine learning, and predictive analytics challenges supporting the U.S. Navy. This is an on-site position.
We are particularly interested in candidates who have completed a Master’s degree, are currently pursuing a PhD, or have completed a substantive capstone, thesis, research project, internship, or academic project utilizing data science, machine learning, statistical modeling, or related technologies. You do not need years of industry experience to be successful in this role. We are looking for strong technical foundations, hands on experience gained through academic or research work, intellectual curiosity, and a desire to build a career developing AI centric solutions.
As a member of FTI’s data science team, you will work alongside experienced Data Scientists and Software Engineers to develop, test, and deploy advanced predictive analytics models. The team uses complex data sets to help identify and predict precursors to Naval mishaps and safety hazards.
This is a hybrid position with onsite work at the Naval Safety Command Center in Norfolk, VA. An Active DoD Secret clearance or above is required. You must be in in the Norfolk, VA commutable area. All final interviews are in person.
Responsibilities
What You Will Work On
- Support the design, calibration, and testing of predictive risk models used to evaluate mishap risk across Navy communities.
- Analyze complex data to identify relationships, patterns, anomalies, and potential predictors using techniques such as time series analysis, forecasting, statistical modeling, causal inference, and anomaly detection.
- Perform feature engineering, cross validation, and model performance evaluation using measures such as precision and recall.
- Apply Python, R, or similar programming languages to real world data science challenges.
- Work with tools and libraries such as Pandas, NumPy, Scikit learn, SciPy, or comparable technologies.
- Partner with experienced Data Scientists and Software Engineers to transition analytical models from development into operational environments.
- Participate in technical exchange meetings and gain exposure to the deployment, maintenance, and interpretation of operational AI and predictive analytics solutions.
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
What We Are Looking For
- Active Department of Defense Secret Clearance.
- Master’s degree completed or in progress in Data Science, Statistics, Mathematics, Computer Science, Operations Research, or a related technical field. Current PhD students are strongly encouraged to apply.
- Hands on data science or analytics experience gained through graduate research, a capstone project, thesis, internship, research assistantship, or other substantive academic project.
- Experience using Python, R, or a similar programming language.
- Exposure to machine learning and analytical libraries such as Pandas, NumPy, Scikit learn, SciPy, or related tools.
- Foundational knowledge of areas such as regression analysis, probability distributions, hypothesis testing, simulation, Bayesian modeling, machine learning, or predictive analytics.
Additional Experience That Would Be Valuable
- Experience does not need to come from a traditional job. Academic projects, research, internships, and capstone work all count.
- Exposure to data visualization or dashboard development using Qlik, Tableau, Python based visualization tools, or similar technologies is a plus. Experience with Databricks or Apache Spark is also beneficial.
Why This Opportunity
For someone early in their career, this is an opportunity to move beyond academic exercises and apply data science, machine learning, and AI to meaningful operational problems. You will work alongside experienced technical professionals, gain exposure to real world AI centric solutions, and build experience supporting a mission focused customer.
If you recently completed your Master’s degree, are currently pursuing your PhD, or completed a capstone or research project that aligns with this work, we would like to hear from you.
#LI-EB1
#LI-Onsite
