Data Scientist

We are seeking a Data Scientist - Enterprise Data and AI Solutions to join our Hyperautomation team. This role is designed for an analytically curious, technically versatile data scientist who can discover, correlate, enrich, and operationalize enterprise data in support of complex business, operational, security, and modernization use cases. The successful candidate will work across enterprise platforms such as Splunk, ServiceNow, Databricks, and related data and automation tools to identify where relevant data resides, evaluate its reliability, reconcile conflicting records, and translate findings into repeatable analytics, AI-enabled enrichment capabilities, dashboards, pipelines, and automated workflows. This position goes beyond predefined reporting. It requires someone who can start with an ambiguous objective, investigate multiple systems, determine what data can and cannot support, and apply data science, analytics, artificial intelligence, machine learning concepts, and automation to produce defensible and scalable solutions. **This role is hybrid and reports onsite in Washington, DC at least 1 day a week and as required for meetings, testing or other gov activities as directed by their lead.** **Key Responsibilities:** - Data Discovery and Analytics: Lead investigative data-discovery and analytics efforts when the required data source, field, or solution path is not yet defined. - Enterprise Platform Analysis: Investigate Splunk, ServiceNow, Databricks, and other enterprise data sources to identify relevant indexes, sourcetypes, tables, APIs, fields, relationships, and authoritative records. - Data Correlation and Reconciliation: Identify correlation keys across configuration management, endpoint, identity, asset, application, security, and operational datasets; reconcile incomplete, inconsistent, duplicated, or conflicting records. - Advanced Querying and Scripting: Develop and optimize searches, queries, scripts, and analytical workflows using SPL, SQL, Python, REST APIs, JSON, and structured or semi-structured data. - AI-Enabled Data Enrichment: Use approved artificial intelligence and generative AI capabilities, including prompt-based APIs, to classify, normalize, extract, infer, and generate missing data points from available record-level context. - AI Output Validation: Evaluate generated or inferred data for accuracy, consistency, business usability, and traceability before incorporating it into analytics, reporting, or operational processes. - Automation Integration: Partner with data engineering, robotic process automation, Power Automate, and workflow teams to convert discoveries and enrichment processes into repeatable, governed, and sustainable enterprise capabilities. - Communication and Prototyping: Develop prototypes, dashboards, proofs of concept, and visualizations; communicate findings, assumptions, risks, data limitations, and recommendations to technical teams and leadership. ### Qualifications **Required Education & Experience:** - Bachelor’s degree in Data Science, Computer Science, Information Systems, Statistics, Engineering, Analytics, or a related technical discipline and at least 2-5 years of relevant experience. Equivalent practical experience may be considered in lieu of a degree. - Experience performing data science, data analytics, or investigative data-discovery work in enterprise environments where data sources, fields, or technical approaches were not fully predefined. - Hands-on Splunk experience, including SPL development, index and sourcetype discovery, field analysis, lookups, joins, and cross-source data correlation. - Hands-on experience navigating and querying ServiceNow data structures, including CMDB, asset, operational, service-management, or related enterprise tables and APIs. - Strong proficiency in SQL and Python for data retrieval, manipulation, integration, analysis, and automation support. - Experience working with REST APIs, JSON, structured data, semi-structured da…