The Analyst Behind a More Trustworthy Readiness Signal

A stronger readiness signal starts with an analyst willing to challenge what the data appears to say.

As a Junior Data Analyst at SteerBridge, Liis Sam combines curiosity, analytical discipline, and aviation context to help teams better understand the evidence behind predictive-maintenance insights.

"Getting to know the data—really understanding it, including the context behind it, before doing anything with it, everything else starts there."

The spike looked significant.

But before treating it as evidence of unusual aircraft-parts demand, Liis Sam asked a more important question:

Did the data reflect real failure behavior?

Her review indicated that certain data points were producing irregular demand patterns. Rather than accepting the result at face value, Liis raised the issue and worked with SteerBridge data architects and aviation subject-matter experts to examine it more closely.

That experience captures how Liis approaches her work as a Junior Data Analyst at SteerBridge.

Building a forecasting model is only part of the job. The result must also be understandable, reproducible, and grounded in the operational context it is intended to support.

Looking Beyond the Model

Liis builds forecasting models that help identify when and which aircraft parts may be more likely to require unscheduled replacement in support of the KC-130 project.

The work contributes to predictive-maintenance analysis by giving the aviation data team another source of evidence for determining what may warrant further investigation.
But a model does not create readiness on its own.

The information behind it must be credible. The analytical approach must fit the problem. Assumptions and limitations must be clear. The result must be considered within the aviation environment it is meant to support.

For Liis, that process starts with understanding the data.

Making the Work Reproducible

Liis approaches analysis with a straightforward standard: another qualified person should be able to understand what she did, run the same process, and reach the same result.

When determining whether her work is accurate, useful, or ready to share, she checks the evaluation metrics agreed upon by the team. She documents her approach, reviews the output, and makes sure the process can be reproduced.

“Someone else should be able to run what I ran and land on the same results.”

That discipline matters because an analytical output is useful only when others can understand what it shows—and what it does not.

Liis’s role is not to make an operational decision. It is to help ensure that the people responsible for the decision can evaluate the evidence, context, assumptions, limitations, and uncertainty behind the result.

Her work includes Python-based analysis and modeling using Jupyter, JupyterLab, PyCharm, and VS Code, with AWS Redshift supporting the data environment. She also works with gradient-boosted modeling approaches, including CatBoost, XGBoost, and LightGBM.

But the value does not come from the tools alone.

It comes from how thoughtfully they are applied.

MCAS Cherry Point Visit

What Makes a Readiness Signal Trustworthy?

A readiness signal becomes more useful when the people responsible for evaluating it can understand the information behind it.

That requires:

Quality data and known sources
Relevant operational context
Agreed-upon evaluation metrics
Clearly communicated assumptions and limitations
Documented and reproducible analysis
Input from technical and aviation experts

Liis helps support that foundation through disciplined analysis, modeling, and cross-functional collaboration.

About Liis Sam

Role: Junior Data Analyst

Location: Vienna, Virginia / Washington, D.C. region

Focus: Forecasting models, predictive-maintenance analysis, data quality, and aviation-readiness analysis

Technical environment: Python, Jupyter and JupyterLab, PyCharm, VS Code, AWS Redshift, CatBoost, XGBoost, and LightGBM

Career path: Transitioned from international law into data science with support from a Hiring Our Heroes fellowship and professional technical training

SteerBridge in three words: Steadfast. Collaborative. Innovative.

When Domain Knowledge Changes the Analysis

Data-quality issues do not always announce themselves clearly in a table or chart.

Sometimes they emerge only when an analyst works with someone who understands the aircraft, maintenance environment, supply process, or operational workflow behind the information.

That is why Liis collaborates with data architects, project leads, stakeholders, and aviation subject-matter experts.

The irregular demand spike was one example.

Liis identified data points producing patterns that did not appear consistent with observed failure behavior. Her finding prompted additional review by technical and aviation experts and helped the team strengthen the information available to downstream analysts.

The experience reinforced an important lesson: a credible result depends on both analytical judgment and operational context.

Seeing the People Behind the Data

For Liis, that context became more tangible during a visit to Marine Corps Air Station Cherry Point.

She spoke with Marines involved in supply and maintenance work, heard from pilots, and saw the aircraft her team supports.

The experience changed how she viewed the information on her screen.

Rows, timestamps, demand histories, and model outputs were no longer abstract. They were connected to maintainers, supply professionals, aircrews, and other aviation teams working to keep aircraft ready.

The visit reinforced an important part of the SteerBridge employee experience:

Analysts do not have to work at a distance from the mission.

They work alongside people who understand the operational environment and the decisions the analysis is intended to inform.

An Unconventional Path Into Data Science

Liis’s path to aviation analytics was not linear.

Before moving into data science, she worked in international law. Her transition was supported through a Hiring Our Heroes fellowship and a program that funded her completion of the Google Data Analytics Professional Certificate.

That opportunity helped her build the foundation for a new technical career.

Since joining SteerBridge, Liis has expanded her coding and data-science skills, developed knowledge of aviation maintenance and supply, and learned how different stakeholders evaluate technical work.

She has also learned that growth requires both individual initiative and a strong team.

“No one succeeds alone.”

Autonomy Without Isolation

Liis describes SteerBridge as a place where employees are trusted to work through difficult problems without being expected to solve them in isolation.

She values the balance between autonomy and collaboration. Team members have room to explore an issue, test an approach, and take ownership of their work. At the same time, organized communication and cross-functional review help keep people aligned.

Good work is recognized. Questions are encouraged. When something does not go as planned, the team examines what happened and carries the lesson into the next effort.

When asked to describe SteerBridge in three words, Liis chose:

Steadfast. Collaborative. Innovative.

Her advice to prospective candidates is equally direct: be curious, be determined, and be willing to figure things out.

But individual drive is only part of the equation.

The people who thrive at SteerBridge also share information, ask for input, and show up for the team.

Building Confidence in the Signal

Liis’s work brings together the parts of data science she finds most meaningful: solving difficult puzzles, continuing to learn, working with people who bring different expertise, and staying connected to the mission behind the analysis.

Her contribution is not simply a model or a chart.

It is the discipline to question a pattern before someone relies on it.

It is the documentation that allows another analyst to reproduce the result.

It is the collaboration that connects technical evidence to aviation reality.

And it is the judgment to recognize that before action can follow, the signal must first be understood.

// Employee Spotlight

Steadfast. Collaborative. Innovative.

Liis' advice to candidates is clear: be curious, be determined, and be willing to figure things out.

But this is not a lone-wolf environment. Collaboration, communication, and showing up for the team matter just as much as individual drive.

Join a mission-driven team.

Explore open roles supporting mission-driven technology: Join the Team

Learn more about SteerBridge Aviation Readiness Platforms

About STEERBRIDGE

At SteerBridge, our vision is to be the trusted partner in delivering transformative solutions that empower our clients to navigate complex challenges and seize opportunities for growth.

Rooted in our core values of integrity, innovation, and engaged leadership, we strive to elevate the standards of service within the government contracting community.

Mike Kropiewnicki
Aug 17, 2026, 7:00:03 AM