• Build and operationalize models for anomaly detection, forecasting, early incident warning, performance regression detection, saturation/capacity risk, and service health scoring. • Correlate logs/metrics/traces/events with topology, deployment, change, and business signals to identify drivers of degradation and reduce time-to-diagnosis. • Prototype and advance agentic workflows that assist with triage, signal enrichment, event clustering, summarization, and guided next-best-action recommendations. • Use and extend enterprise observability platforms (Splunk, Datadog, Cribl, SolarWinds, Langfuse) to extract signals, engineer features, validate hypotheses, and operationalize outcomes. • Define data quality checks, feature pipelines, and scalable methods for working with high-volume telemetry (batch and/or streaming), partnering with platform teams as needed. • Establish model performance measures aligned to operational goals (noise reduction, precision/recall of detections, lead time to failure, MTTR improvements); monitor drift and iterate. • Communicate findings to technical and non-technical stakeholders with clear recommendations, tradeoffs, and measurable results. • Ensure solutions align with Leidos standards for security, privacy, governance, and responsible AI practices.

• Analyze and model large, sometimes complex, datasets, including web traffic, user behavior, engagement funnels, and video and content metadata • Develop and deploy scalable machine learning models for forecasting, user segmentation, and content performance optimization tasks • Design, build, and maintain scalable ETL pipelines and workflows using GCP and BigQuery for data ingestion and transformation • Leverage machine learning and AI techniques to improve decision-making, including experimentation, ranking, scoring and automation use cases • Collaborate with product, business verticals, operations and engineering teams to align initiatives with technical and business objectives and KPIs • Build dashboards and visualizations that provide visibility into performance, tends and opportunities across the business • Translate insights into clear recommendations that influence strategy and prioritization to stakeholders • Optimize queries and workflows in SQL and Python for performance, reliability and efficiency • Continuously evaluate new capabilities and tools to improve efficiency, insight generation and business outcomes

• Provides CDM leadership for one or more assigned projects or indications dependent on size and scale of the project • Lead studies including (but not limited to) a combination of healthy volunteer and patient populations, multi-site, complex protocol design, strong client management required or reduced timelines • Demonstrates leadership and operational expertise in the strategic planning and delivery of CDM deliverables at program and/or project level • Management and oversight of vendor contracts, resourcing and budget management and oversight of vendor performance for assigned programs, and projects • Communicates and negotiates effectively with all other Program level team members • Primary point of contact for Clinical Data Management (CDM) • Provide oversight and expertise of external service providers or in-house teams to deliver quality data with compliance to study model procedures and standards; Responsible for proactive risk management and issue resolution/escalation connected to Clinical Data Management improvement or technology • Develops an understanding of CDASH and SDTM or other recognized industry standards and impact to programming team to ensure consistency of program level standards

• Lead large-scale government data projects, including cross-functional team coordination, client relationship management with federal agencies, and budget management. • Create and implement record linkage algorithms using Python, integrating administrative records and survey data to enhance the value of government agency data products. • Modernize outdated government data systems by transitioning legacy codebases from SAS/STATA to R/Python. • Develop and deliver educational materials and training programs on Python, R, and SQL coding best practices to enhance government agency staff capabilities. • Train and mentor government analysts at federal and state agencies, strengthening data science analytical capabilities across agencies. • Build and maintain interactive dashboard visualizations using tools like R Shiny to deliver dynamic, data-driven reports that inform agency leadership and policymakers in decision-making. • Conduct text analysis and natural language processing (NLP) to extract insights from unstructured data. • Author and contribute to federally funded reports and research papers documenting the impact of government data science initiatives.