Job DescriptionDescription 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 data, and enterprise system integrations.
- Practical experience applying artificial intelligence, generative AI, machine learning concepts, prompting, entity resolution, classification, normalization, or enrichment techniques to real-world data problems.
- Ability to validate generated, inferred, or enriched data; document assumptions and limitations; and distinguish authoritative source data from derived or AI-generated information.
- Strong analytical, problem-solving, documentation, and communication skills with the ability to work independently in ambiguous environments.
Required Clearance:- US Citizenship.
- Active Secret Clearance.
Preferred Qualifications:- Experience with Databricks, Apache Spark, Delta Lake, cloud-based lakehouse architectures, or large-scale enterprise data manipulation.
- Experience integrating with AI or large language model services through APIs, including prompt design, structured outputs, response evaluation, and exception handling.
- Experience developing or supporting workflows using Microsoft Power Automate, UiPath, ServiceNow Flow Designer, or comparable automation platforms.
- Experience operationalizing AI-enriched data through pipelines, dashboards, workflow tools, or human-in-the-loop review processes.
- Familiarity with retrieval-augmented generation, semantic matching, embedding models, vector databases, or related information-retrieval techniques.
- Experience with Splunk Machine Learning Toolkit, ServiceNow IntegrationHub, ITOM, ITAM, or related enterprise capabilities.
- Experience working in federal government, regulated-industry, cybersecurity, IT asset management, or large-scale enterprise environments.
Target salary range: $80,001 - $120,000. The estimate displayed represents the typical salary range for this position based on experience and other factors.
Overview SAIC accepts applications on an ongoing basis and there is no deadline.
