Mid-Data Scientist

SteerBridge•Vienna, VA

2d•$137,000 - $155,000•Hybrid

About The Position

SteerBridge is seeking a Mid-Data Scientist to analyze multidimensional aviation, global supply chain, and operational data to develop and maintain predictive models. The ideal candidate has hands-on experience with statistical modeling, machine learning, and time-series analysis and is comfortable working with complex data from multiple sources and formats. Candidates must have hands-on experience with supervised learning, including classification and regression, and unsupervised learning, including clustering and dimensionality reduction. This role requires a quick learner who is curious, innovative, results-oriented, and able to identify meaningful trends and patterns within large, complex datasets. Strong interpersonal skills and the ability to work effectively with partners are also essential.

Requirements

Must be a U.S. Citizen.
MSc or PhD degree in applied mathematics, statistics, or relevant work experience.
An active security clearance or the ability to obtain one is required.
Collaborate with various stakeholders to understand requirements and translate those requirements into data science solutions.
Provide guidance on best practices and industry standards across data science and analytics, data visualization, and share expertise to improve technical capabilities of the team.
Design, develop, and integrate templates, data, and models for repeatability.
Develop and implement data quality assurance and management protocols.
Create, maintain, and organize technical documentation for all data collection, cleaning, and analyses.
Must be familiar with multiple types of data models including, but not limited to, generalized linear and multilinear regression, logistic and multinomial regression, and time series analysis.
Must have hand-on experience with supervised (classification, regression) and unsupervised learning (clustering, dimension reduction).
Must have experience documenting algorithm development and implementation.
4+ years of experience evaluating relationships in data using statistical modeling and leveraging analytics tools.
4+ years of experience in advanced Classification and Regression modeling.
4+ years of professional proficiency using R, or Python for data wrangling and model building.
Experience in SQL or Spark SQL, and basic database design.
Demonstrated high proficiency in statistical analysis and data visualization.
Demonstrated high proficiency in data wrangling and documentation.
Solid technical skills across a wide variety of tools and data platforms.
Able to successfully prioritize and manage multiple project tasks simultaneously and complete them in a timely manner with a high degree of accuracy.
Strong record of applied data analysis.
Excellent writing and presentation skills with a successful track record of communicating complex concepts to diverse audiences.

Nice To Haves

3+ years in the aerospace industry preferred.
Cloud project work using Google, AWS and/or Azure.
Aviation Background preferred.
(Highly preferred) AWS or Google Cloud Professional or Specialty Certification or ability to obtain certification.
Experience with supply chain management data systems and technology is desirable (e.g., ERP, Transportation Management and Warehouse Management systems).
Experience supporting DoD and/or VA missions.
Proficiency in integrating and interfacing with software development processes.
Consulting experience.
RAG, Embedding, Vector DB, hugging face transformer, BERT, BART, LLMs

Responsibilities

Develop, evaluate, and maintain predictive and statistical models.
Lead or support end-to-end analytical modeling and data science framing activities.
Analyze and integrate large, complex datasets from multiple sources and formats.
Apply regression, time-series analysis, and supervised and unsupervised machine learning techniques.
Work with partners to identify data fields, sources, and integration requirements.
Assess data quality, provenance, and suitability for modeling.
Identify and communicate relevant trends and patterns within complex datasets.
Document data processes, methodologies, and models.