Data Scientist

** Description*
* The Leidos Digital Modernization Sector is seeking a Data Scientist; this position will allow for full time telework from any U.S. based location.

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* POSITION SUMMARY:

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* We are seeking a skilled Data Scientist with deep expertise in ETL (Extract, Transform, Load) pipeline development and process mining to join our Operations Excellence team. In this role, you will serve as the primary data engineer and analyst responsible for extracting and integrating data from a broad range of enterprise systems, applications, and databases, and loading it into our IBM Process Mining platform to enable accurate, actionable process models.

This is an iterative, high-impact role: once an initial process model is produced, you will collaborate with process analysts and stakeholders to identify data gaps, refine extractions, and rebuild models to continuously improve fidelity. You will also work with task mining outputs, including desktop event logs, to supplement process discovery efforts.

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* PRIMARY RESPONSIBILITIES:

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* ** _ETL Development & Data Integration_*
* + Design, build, and maintain ETL pipelines to extract data from diverse enterprise sources including ERP systems, CRM platforms, relational databases (SQL Server, Oracle, Postgre

SQL), flat files, and APIs.

+ Transform raw data into structured event logs and case tables conforming to IBM Process Mining ingestion requirements (XES, CSV, or direct connector formats).

+ Perform data profiling, cleansing, deduplication, and normalization to ensure high-quality inputs for process model development.

+ Manage iterative data pulls as process models are refined and additional data requirements are identified by analysts and stakeholders.

** _Process Mining & Model Development_*
* + Load event log and transaction log data into IBM Process Mining and configure case, activity, and timestamp mappings.

+ Collaborate with process analysts to build, validate, and iterate on process models that accurately reflect real-world workflows.

+ Support root cause analysis, conformance checking, and performance analysis within IBM Process Mining.

+ Identify bottlenecks, deviations, and rework loops in process data and communicate findings to business stakeholders.

** _Task Mining & Event Log Analysis_*
* + Ingest and process desktop activity/event logs generated from task mining tools, mapping UI interactions to higher-level process steps.

+ Correlate task mining data with system-level transaction logs to produce enriched, end-to-end process views.

+ Validate task mining event structures and resolve data quality issues prior to model ingestion.

** _Collaboration & Documentation_*
* + Partner with IT, database administrators, and application owners to gain data access and understand source system schemas.

+ Document ETL pipelines, data dictionaries, transformation logic, and model configurations for repeatability and auditability.

+ Communicate data limitations, assumptions, and model constraints clearly to non-technical stakeholders.

+ Support process improvement initiatives by providing data-driven insights derived from process model outputs.

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* BASIC QUALIFICATIONS:

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* + Bachelor's degree in Data Science, Computer Science, Information Systems, Statistics, or a related technical field; additional years of experience may be substituted in lieu of a degree.

+ 8+ years of experience in ETL development, data engineering, or data integration roles.

+ Proficiency in SQL for complex querying, joins, and data transformation across multiple relational database platforms (SQL Server, Oracle, MySQL, Postgre

SQL).

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Experience with Python or R for data manipulation, scripting, and automation (pandas, Num Py, SQL Alchemy, etc.).

+ Demonstrated experience working with event logs, transaction logs, or time-series data in an analytical context.

+ Familiarity with process mining concepts including case , activity, timestamp structures, and event log formats (XES or equivalent).

+ Strong data profiling and data quality assessment skills.

+ Ability to work iteratively in a fast-paced environment where requirements evolve based on model outputs.

+ US citizenship required.

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* PREFERRED QUALIFICATIONS:

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