Senior Data Scientist Engineer Security Clearance

Position: Senior Data Scientist Engineer with Security Clearance
About Four Sea Group, Inc. Four Sea Group is a 100% employee-owned technical mission support company based in Denver, Colorado. Founded by an aerospace industry veteran and PhD astrophysicist, we support complex defense, space, software, systems, and mission-focused programs for government and industry partners. What sets FSG apart is the caliber of our people and the way we operate: we are a reputation-driven team of highly skilled engineers and technical experts who are brought in for their depth, judgment, and ability to solve difficult problems.

As an employee-owned company, we have built our compensation, benefits, and culture around a simple idea: when the company succeeds, that value should go back to the people doing the work. About the role We are seeking a Senior Data Scientist Engineer to lead the design, development, and deployment of advanced AI/ML solutions supporting complex mission and operational environments. This role is ideal for an experienced engineer who can operate independently in ambiguous technical settings, guide high-impact model development, and help translate raw data into actionable intelligence through scalable, production-ready machine learning solutions.

The ideal candidate brings deep experience in applied machine learning, strong technical judgment, and the ability to collaborate across engineering and analyst teams while mentoring other engineers and improving the reliability, explainability, and operational readiness of deployed models. What you'll do
* Lead development, training, optimization, and deployment of advanced AI/ML models in support of mission and operational objectives.
* Define and implement technical approaches for model development, validation, explainability, monitoring, and lifecycle management.
* Build and improve automated data ingestion, curation, retraining, and model delivery pipelines to support long-term adaptability and performance.
* Operationalize machine learning models in production environments and support integration with broader software and Dev Sec Ops  workflows.
* Monitor model performance, implement drift detection and testing approaches, and improve reliability through continuous evaluation and optimization.
* Collaborate with analysts, mission experts, software engineers, and other stakeholders to validate model outputs and align solutions to operational needs.
* Guide technical decision-making related to model performance, interpretability, reproducibility, and deployment tradeoffs.
* Review code, mentor mid-level engineers, and promote strong engineering practices across the team.
* Contribute to continuous improvement in data science methodology, model governance, and production ML execution.

Required Qualifications
* Bachelor's degree or higher in a quantitative discipline such as statistics, mathematics, operations research, engineering, computer science, or a related technical field.
* 10+ years of relevant experience designing, training, and deploying machine learning models in complex technical environments.
* Active TS/SCI clearance with CI Polygraph required at time of application.
* Experience leading complex AI/ML efforts and operating independently in technically ambiguous, high-consequence environments.
* Strong experience with supervised, unsupervised, and ensemble modeling approaches, including neural network-based methods.
* Experience developing and deploying production machine learning solutions, not just research prototypes.
* Experience with model validation, performance monitoring, explainability, and reproducibility practices.
* Proficiency with Python and common data science and machine learning libraries/frameworks.
* Experience collaborating across engineering, analyst, and operational stakeholders to align models to mission needs.
* Strong written and verbal communication skills.

Preferred Qualifications
* Background in deep learning approaches such as CNNs, RNNs, Transformers, or semi-supervised learning methods.
* Proficiency with frameworks and libraries such as Tensor Flow, PyTorch, Scikit-learn, XGBoost, or similar tools.
* Familiarity with distributed data or compute frameworks including Spark, Ray, or Dask.
* Working knowledge of ML lifecycle and deployment tooling such as MLflow, DVC, Docker, or similar environments.
* Knowledge of model monitoring, A/B testing, drift detection, and long-term production support practices.
* Analytical depth in areas such as causal inference, signal data processing, experiment design, or model interpretability approaches including SHAP or LIME.
* Comfort working in Agile or Dev Sec Ops  environments using tools such as Git, Jupyter Notebooks, Confluence, or similar collaboration platforms.
* Demonstrated success mentoring engineers or guiding technical work in applied AI/ML environments.
* Prior work in the DoD or Intelligence Community supporting mission-critical data science applications.
* Exposure to enterprise AI initiatives, production ML governance, or…