Data Scientist

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.

• →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.

• 3+ years of experience in the aviation or aerospace industry.

• AWS or Google Cloud Professional or Specialty Certification, or the ability to obtain certification, highly preferred.

• Experience with supply chain management data systems and technologies, including ERP, Transportation Management Systems (TMS), and Warehouse Management Systems (WMS).

• Experience supporting DoD and/or VA missions and/or working in a consulting environment.

• Experience with software development processes and modern AI and machine learning technologies, including Retrieval-Augmented Generation (RAG), embeddings, vector databases, Hugging Face Transformers, BERT, BART, and large language models (LLMs).