Senior Data Scientist

About this role

Three Sisters Federal i s part of the Seneca Nation Group (SNG) portfolio of companies . SNG is Seneca Holdings' federal government contracting business that meets mission-critical needs of federal civilian, defense, and intelligence community customers. Our portfolio comprises multiple subsidiaries that participate in the Small Business Administration 8(a) program. To learn more about SNG, visit the website and follow us on LinkedIn .

Our team of talented individuals is what makes us successful. To support our team, we provide a balanced mix of benefits and programs. Your total rewards package includes competitive pay, benefits, and perks, flexible work-life balance, professional development opportunities, and performance and recognition programs. We offer a comprehensive benefits package that includes medical, dental, vision, life, and disability, voluntary benefit programs (critical illness, hospital, and accident), health savings and flexible spending accounts, and retirement 401K plan. One of our fundamental principles is to offer competitive health and welfare benefits to our team members, providing coverage and care for you and your family. Full-time employees working at least 30 hours a week on a regular basis are eligible to participate in our benefits and paid leave programs. We pride ourselves on our collaborative work environment and culture, which embraces our mission of providing financial and non-financial benefits back to the members of the Seneca Nation.

About the Role

Three Sisters Federal is seeking a Senior Data Scientist to support a Department of Veterans Affairs (VA) Veterans Health Administration customer responsible for workforce learning, education, and development across the largest integrated health system in the United States. The customer converts raw training and workforce data into reporting that leadership, program offices, and congressional inquiries rely on.

This is a hands-on individual contributor role working on a small team. The work is roughly evenly split between data engineering (SQL Server ETL, pipeline modernization, data quality) and analytics delivery (Power BI semantic models, dashboards, statistical analysis, rapid-turnaround data calls). The environment today is on-premises SQL Server with Power BI; a meaningful part of this role is helping move it forward.

We are looking for someone who does excellent analytical work and can also design the environment that analytical work depends on. Decisions about tooling, infrastructure, and platform direction rest with the customer, so the value here is in arriving with options rather than assumptions: laying out approaches with their trade-offs, cost and access implications, and migration paths, in enough detail that a decision can be made, and then implementing what is approved. Candidates who have thought through how to set up an analytics environment, and not only how to work inside one someone else built, will be a strong fit.

Beyond sustaining current reporting, our preferred candidate can expand this role by maturing the analytics platform and the engineering practices around it. That work may include automating data cleansing in Python at the point of arrival and retiring the cursor-based cleansing routines that currently run inside SQL Server; standing up source control and dependency management so analytical code is reproducible across machines; extending visualization beyond native Power BI through programmatic charting delivered as Python visuals inside Power BI reports, with interactive and publication-quality table output available outside the report canvas; and connecting to source systems through Python database connectors rather than manual extracts. While SSIS remains in place, but pipeline orchestration could move outside SQL Server, with options like Apache Airflow and Microsoft Fabric among the candidates under evaluation, and with credential management handled as a deliberate part of the design rather than an afterthought. The role could also maintain a technical backlog covering planned engineering work, nice-to-have improvements, and identified deficiencies affecting data security or data quality, so remediation and enhancement are sequenced deliberately rather than handled as each request arrives.

Responsibilities

Data Engineering and Pipeline Modernization

• Build and maintain a SQL Server data warehouse, including ETL processes sourcing from multiple enterprise applications and SharePoint.

• Replace cursor-based data cleansing routines in SQL Server with Python-based processing.

• Catalog and document existing ETL workflows and their interdependencies to reduce technical debt.

• Design pipeline orchestration outside of SQL Server, including credential and secrets management for scheduled jobs and data connections; evaluate candidate tools against the customer's constraints, present recommendations, and implement the approved approach.

• Establish source control and dependency management practices so analytical code runs reproducibly across machines and environments.

• Design a repeatable deployment process for ETL and analytics code, moving changes from development through test to production with peer review, versioned releases, and a rollback path, and document what tooling and access each option would require so the customer can decide what to stand up.

Analytics and Modeling

• Conduct statistical analysis, regression, and predictive modeling to answer organizational questions and support projections.

• Apply machine learning and text analytics methods where they demonstrably add value, including classification, forecasting, and segmentation, and make the ROI case before committing to an approach.

• Perform exploratory analysis on new and archival data sources to surface trends that conventional reporting misses.

• Design and implement data quality controls, including automated validation.

• Evaluate model performance and refine methods over time.

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