• Partner directly on growth initiatives to build, ship, measure incrementality of bookings/attach rate/revenue, and optimize 0-to-1 customer-facing features • Thought partnership with Product to define strategy, sequence iteration, scale impact via science frameworks and as the go-to tech lead around data, experimentation, and domain understanding • Build observational causal inference models to deeply understand guest intent, identify underlying mechanisms, and build data products to measure impact, and inform feature objectives and tradeoffs • Champion AI-enabled Product Understanding, to scale data-driven frameworks, robust measurement, and compelling storytelling of data learnings to guide recommendations and roadmaps with senior leaders

• Analyze large-scale manufacturing data, including **sensor, batch, recipe, and production line data** • Develop analytics solutions to identify **defects, scrap, rework, and process deviations** • Perform **root cause analysis** and **multivariate process analysis** to uncover drivers of quality issues • Build **anomaly and defect detection models** to proactively identify process failures • Partner with manufacturing, quality, and operations teams to translate findings into actionable improvements • Deliver measurable outcomes such as **cost reduction, waste minimization, and quality improvement**

• Analyze large volumes of structured and unstructured data. • Design, develop, and implement statistical models and machine learning algorithms to predict outcomes and solve complex business problems. • Collect, clean, and preprocess data from multiple sources. • Develop dashboards and data visualizations to effectively communicate findings and insights. • Collaborate closely with IT, marketing, and other business teams to understand requirements and deliver data-driven solutions. • Stay up to date with industry trends, tools, and technologies in data science to continuously improve processes and products.

• 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.