Data Scientist (Level 3 or4)

Data Architecture & Modeling - Design scalable data mining pipelines, establish protocols for data ingestion, cleaning, transformation, and storage. Evolve models to capture long‑term trends and support real‑time analytics. Statistical Insight - Perform exploratory data analysis, hypothesis testing, and advanced statistical modeling to identify key drivers and associations within heterogeneous data sources. Visualization & Reporting - Create interactive dashboards and reports using modern BI and visualization tools (e.g., Tableau, Power BI, Plotly) to communicate insights to technical and non‑technical stakeholders. Cross‑Functional Collaboration - Partner with engineering, operations, and business units to define problem statements, translate requirements into analytical solutions, and integrate findings into product roadmaps. Innovation & Knowledge Leadership - Stay abreast of cutting‑edge data science methodologies (e.g., deep learning, reinforcement learning) and evaluate their relevance to defense contexts. Mentor teams and promote a culture of data‑driven decision making. Governance & Quality - Establish data governance standards, data quality metrics, and reproducibility practices across the analytics lifecycle. Bachelor's degree in related STEM discipline with 5 years of experience; Master's degree related in STEM discipline with 3 years of experience; PhD in related STEM discipline with 1 year of experience in Electrical Engineering, Systems Engineering, Software Engineering, Computer Engineering, Physics, Mathematics or related technical fields; an additional 4 years of experience may be considered in lieu of a degree. Expertise in statistical modeling (regression, classification, time series) and machine learning (supervised/unsupervised) Proficient in Python/R, SQL, and big‑data frameworks (Spark, Hadoop) Principal Data Scientist: Bachelor's degree in related STEM discipline with 8 years of experience; Master's degree related in STEM discipline with 6 years of experience; PhD in related STEM discipline with 4 years of experience in Electrical Engineering, Systems Engineering, Software Engineering, Computer Engineering, Physics, Mathematics or related technical fields; an additional 4 years of experience may be considered in lieu of a degree. Expertise in statistical modeling (regression, classification, time series) and machine learning (supervised/unsupervised) Proficient in Python/R, SQL, and big‑data frameworks (Spark, Hadoop) Bachelor's degree in the STEM field of study. Excellent communication: ability to translate complex analytics into clear, actionable insights Leadership: mentoring, influencing cross‑functional teams, driving analytical strategy Working knowledge of Cybersecurity technologies, NIST standards, and RMF governance Strong analytical and problem‑solving abilities Proven ability to influence and drive adoption of best practices across teams SAP access