Data Scientist II at Falconwood, Inc.

Norfolk, Virginia, United States -

Full Time

Start Date

Immediate

Expiry Date

26 Jun, 26

Salary

180000.0

Posted On

28 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, SQL, Pandas, Scikit-learn, TensorFlow, Databricks, Spark, Delta Lake, Power BI, Jupyter, SAFe, Agile, DoD Data Governance, Risk Modeling, Predictive Analytics, AI

Industry

Defense and Space Manufacturing

Description

Overview Falconwood is a woman-owned, veteran-owned company providing consultation and programmatic support to Department of Defense Information Technology (IT) Initiatives and programs. We provide expert advice and consultation on a diverse range of IT subjects, focusing on acquisition, policy, cybersecurity, engineering, and process development. The Data Scientist II the Department of the Navy’s enterprise IT rationalization and sustainment planning initiatives, with a focus on data-driven decision automation, World-Class Alignment Metrics (WAM) integration, and system lifecycle modeling. This role requires hands-on experience working with large and complex datasets to identify operational inefficiencies, model risk-based divestment triggers, and visualize performance trends across enterprise IT tools. The ideal candidate will contribute to real-time dashboards, predictive analytics, and AI-enabled sustainment planning while working alongside analysts, engineers, and Navy leadership in a secure, agile environment. Responsibilities Analyze authoritative Navy IT datasets (e.g., CLIN33, ATO status, system inventories) to inform sustainment and divestment decisions. Build predictive models to forecast obsolescence, security risk, and performance degradation using structured WAM-aligned features. Integrate and normalize disparate data sources (e.g., eMASS, DADMS, licensing data, helpdesk logs) for analysis readiness. Collaborate with technical SMEs and stakeholders to refine automated scoring models and lifecycle staging criteria. Develop and maintain visualization assets (e.g., Power BI, Jupyter dashboards) to communicate analytical insights. Support iterative Agile cycles under the SAFe framework, contributing to sprint demos and feedback sessions. Translate analytical findings into actionable insights and technical recommendations for government stakeholders. Contribute to a growing body of reusable logic, machine learning workflows, and AI-enriched rationalization tools. Qualifications Required: 5+ years of applied experience in data science, with proven delivery in analytics or decision-support environments. Proficiency in Python, SQL, and data science libraries (e.g., Pandas, Scikit-learn, TensorFlow). Demonstrated ability to work with large-scale data platforms such as Databricks, Spark, or Delta Lake. Experience developing models or transformations to support ML pipelines and decision automation. Strong grasp of WAM-style metrics, cost modeling, or operational analytics frameworks. Experience with DoD data governance, security practices, and compliance constraints (e.g., IL4/ATO awareness). Active Secret clearance. Preferred: Familiarity with Navy-specific tools such as C-Star, Department of the Navy Jupiter, Advana technology stack, or the Navy Enterprise Network Data Hub. Hands-on experience with Power BI, Tableau, Qlik or other dashboarding tools for stakeholder reporting. Experience with MLflow, Apache Kafka, or Azure-based ML platforms. Background in IT portfolio management, rationalization, or sustainment modeling. Experience with Agile and SAFe development environments. Cloud certifications (e.g., Azure, AWS) or experience deploying models in cloud-native environments. Understanding of REST APIs, ETL/ELT pipelines, and data warehousing concepts (OLAP/OLTP) Pay Range 170000-180000 based on qualifications and relevant experience. Falconwood offers excellentbenefits including health, 401k matching, and continuing education incentives.

Responsibilities

Analyze authoritative Navy IT datasets to inform sustainment and divestment decisions, building predictive models for obsolescence and risk forecasting using WAM-aligned features. Develop and maintain visualization assets and translate analytical findings into actionable recommendations for government stakeholders.