Data Enrichment Remote Jobs (NOW HIRING)

Job ID: 2613789

Location: Washington, DC, US

Date Posted: 2026-06-17

Category: Information Technology

Subcategory: Data Scientist

Schedule: Full-Time

Shift: Day Job

Travel: No

Minimum Clearance Required: Secret

Clearance Level Must Be Able to Obtain: None

Potential for Remote Work: ORA_HYBRID

Description

We are seeking an Enterprise Data and AI Solutions Scientist to join our Hyperautomation team. This role is designed for an analytically curious and technically versatile “data hunter” who thrives when the required data source, system, field, or solution has not yet been identified.

The successful candidate will lead investigative data-discovery efforts across enterprise platforms, determine where relevant information resides, evaluate its reliability, correlate records across disparate systems, and translate findings into repeatable analytics, AI-enabled data-enrichment capabilities, and automated workflows.

This position goes beyond querying known datasets or producing predefined reports. It requires someone who can take an ambiguous business objective, investigate multiple enterprise systems, identify relationships among incomplete or conflicting datasets, and develop an evidence-based approach to solving the problem. This individual will bridge the gap between raw enterprise data, intelligent data enrichment, and automation.

What You Will Bring to the Team

You are more than a reporting specialist or traditional data scientist. You are comfortable beginning with an unanswered question, navigating unfamiliar enterprise systems, testing

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:

• Investigative Data Discovery: Lead data-hunting and investigative analytics efforts in support of complex business, operational, security, and hyperautomation use cases.
• Platform Exploration: Investigate enterprise platforms, particularly Splunk and ServiceNow, to identify relevant indexes, sourcetypes, tables, fields, APIs, relationships, and authoritative data sources.
• Data Correlation and Reconciliation: Identify correlation keys across disparate systems, including configuration-management, endpoint, identity, asset, and operational data. Develop methods for reconciling 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, and related data-retrieval technologies.
• AI-Driven Data Enrichment: Use approved AI and Generative AI capabilities, including prompt-based APIs, to normalize data, extract attributes, and generate missing data points from available record-level context. Examples may include using known IT asset manufacturers and models to determine or infer lifecycle attributes such as End of Life or End of Support.
• AI Output Validation: Evaluate AI-generated attributes for accuracy, consistency, and business usability. Clearly distinguish authoritative source data from inferred or generated information and document supporting evidence, confidence, and known limitations.
• Automation Integration: Partner with RPA, workflow, and data-engineering teams to convert successful analytical discoveries and enrichment processes into repeatable, governed, and sustainable enterprise capabilities.
• Solution Design: Help determine whether a use case is best addressed through data engineering, API integration, business-process automation, robotic process automation, AI-enabled enrichment, or a hybrid approach.
• Prototyping and Communication: Develop prototypes, proofs of concept, dashboards, and visualizations. Communicate findings, data limitations, technical risks, and recommendations to both technical teams and senior leadership.

Core Competencies:

• Investigative Curiosity: A persistent drive to explore unfamiliar systems and data structures until a defensible answer or path forward is identified.
• Systems Thinking: The ability to understand how data flows across applications, infrastructure, identity systems, assets, and business processes.
• Evidence-Based Discipline: A rigorous approach to validating conclusions, documenting data lineage, and distinguishing authoritative, derived, and AI-generated information.
• Solution Orientation: The ability to turn one-time discoveries into reusable, scalable, and supportable enterprise capabilities.
• Consultative Communication: The ability to translate complex data findings into clear, actionable recommendations for technical and business stakeholders.

Illustrative Use Cases:

• Correlating security, endpoint, identity, asset, and service-management data across Splunk, ServiceNow, and other platforms to identify vulnerable, unsupported, or untracked enterprise assets.
• Sending known asset attributes, such as manufacturer, model, product family, and software version, to an approved AI service to generate missing lifecycle information, including estimated End-of-Life and End-of-Support dates.
• Using AI to normalize inconsistent manufacturer names, product models, application titles, organizational values, and other records that cannot be reliably standardized through static rules alone.
• Validating AI-generated data against available evidence, documenting the basis for each determination, and flagging low-confidence or conflicting results for human review.
• Mapping a manual data-gathering and reconciliation process and redesigning it as an automated pipeline using Databricks, APIs, ServiceNow, Splunk, Power Automate, or RPA technologies.

Qualifications

Required Education & Experience:

• Bachelor’s degree in Data Science, Computer Science, Information Systems, Statistics, Engineering, or a related technical discipline and at least 2-5 years of relevant professional experience. Equivalent practical experience may be considered in lieu of a degree.
• Demonstrated experience conducting data discovery or investigative analytics when the required data sources, fields, or technical approach were not 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, or related enterprise tables and APIs.
• Strong proficiency in SQL and Python for data retrieval, manipulation, integration, and analysis.
• Experience working with REST APIs, JSON, and structured or semi-structured data.
• Practical experience using AI, Generative AI, or prompt-based services to extract, classify, normalize, infer, or enrich enterprise data.
• Experience evaluating and validating generated or inferred data before incorporating it into analytics, reporting, or operational processes.
• Ability to work independently in ambiguous environments, formulate and test hypotheses, and adapt based on emerging findings.
• Strong analytical, problem-solving, documentation, and technical communication skills.

Required Clearance:

• US Citizenship.
• Active Secret Clearance.

Preferred Qualifications:

• Experience with Databricks, Apache Spark, Delta Lake, or cloud-based lakehouse architectures.
• 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 or UiPath.
• Experience operationalizing AI-generated or AI-enriched data through automated pipelines, dashboards, workflow tools, or human-in-the-loop review processes.
• Familiarity with Retrieval-Augmented Generation, semantic matching, embedding models, vector databases, entity resolution, or related information-retrieval techniques.
• Experience working in federal government, regulated-industry, cybersecurity, IT asset-management, or large-scale enterprise environments.