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SierTeK

Data Scientist

SierTeK, Beavercreek, Ohio, United States

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SierTeK proudly serves our clients by providing expertise in the Program Management, Information Technology, and Administrative Support domains. Founded in 2007 as a minority and service-disabled veteran-owned company, we serve as prime- and subcontractor for a multitude of Federal Department of Defense contracts. By focusing on continual improvement, our services remain at the forefront of our industry, and we pride ourselves on delivering our services with the highest degree of integrity.

SierTeK Ltd. is seeking a Data Scientist to support an opportunity remotely

.

PLEASE APPLY DIRECTLY ON OUR WEBSITE AT SIERTEK.COM/CAREERS

POSITION OVERVIEW SECTION

This effort includes tasks and activities to be performed by the employee to support the USAFSAM Aerospace Medicine Consultation Division specifically to support clinical and operational data analysis.

Essential Job Functions

Machine Learning Model Development

Develop, deploy, and fine-tune novel unsupervised, tree-based clustering machine learning models

Algorithmic Knowledge & Coding Expertise

Accurately explain and code from scratch:

Decision Trees

K-Nearest Neighbors (KNN) algorithm

Must explain the difference between density-based and binning-based clustering techniques

For KNN:

Able to implement naive version from scratch

If unable to optimize the implementation, must clearly explain optimized versions (e.g., KD-Tree, Ball Tree, Approximate Nearest Neighbor techniques

Demonstrate ability to:

Write an optimized list sorting algorithm from scratch within 1 hour without external references

Generate and minimize distance matrices in code (including writing vectorized implementations)

Data Privacy & Compliance

Obtain HIPAA training upon beginning work

Maintain strict HIPAA compliance to prevent any data breaches or leaks

Qualifications

Minimum Position Requirements

Minimum of a Bachelor's degree in an applied quantitative field such as:

Computer Science

Actuarial Science

Operations Research

Applied Mathematics

Economics

Experience Requirement:

At least three (3) years of experience in deploying predictive analytics.

Technical Skills:

Programming:

Proficiency in Python with object-oriented programming (OOP).

Data Manipulation:

Experience using standard Python data manipulation libraries including:

NumPy

Pandas

Data Visualization:

Experience using Matplotlib for standard plotting and visualizations in Python.

Machine Learning:

Experience with Scikit-learn and other Python open-source machine learning tools.

Additional Requirement:

Non-Disclosure Agreement (NDA):

Must have a signed NDA with their employer due to potential access to confidential, proprietary, or sensitive information.

The NDA must be submitted to the Government Program Manager before the start of performance.

Highly Desired Qualifications

Experience collaborating with software engineers to develop interactive dashboards for AI technologies

Experience in developing with pre-trained language models (e.g., transformers, LLMs)

Familiarity with dimensionality reduction techniques (e.g., PCA, t-SNE, UMAP)

Hands-on experience with deep learning frameworks such as TensorFlow, PyTorch, or other GPU-enabled libraries

Prior experience working with USAFSAM (U.S. Air Force School of Aerospace Medicine)

Educational background or study in human biology

Educational background or study in human performance

SierTeK is an equal opportunity employer. Employment is decided based on qualifications, merit, and business need. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected Veteran status, gender identity and sexual orientation.

This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, transfer, leaves of absence, compensation, and training.

If you need assistance or accommodation due to a disability, you may contact us at 1+833.743.7835.