Aerospace Data Scientist — Predictive ML & Time Series

SteerBridge; Vienna (VA); On-site; USD 137,000 - 155,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

Life Insurance

401(k) Retirement Plan with matching

Paid Time Off

Paid Federal Holidays

Job summary

SteerBridge in Vienna, VA 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.

Requirements include hands-on experience with supervised learning (classification/regression) and unsupervised learning

Qualifications

• Must be a U.S. Citizen.
• MSc or PhD in applied mathematics, statistics, or relevant work experience.
• Active security clearance or ability to obtain one.
• Experience with regression, time-series, and ML techniques.
• Experience documenting algorithm development and implementation.

Responsibilities

• Develop, evaluate, and maintain predictive and statistical models.
• Lead or support end-to-end analytical modeling activities.
• Analyze and integrate large, complex datasets from multiple sources.
• Apply regression, time-series analysis, and ML techniques.
• Collaborate with stakeholders to identify data fields and integration requirements.
• Assess data quality, provenance, and suitability for modeling.
• Identify trends and communicate patterns to diverse audiences.
• Document data processes and models.

Skills

R

Python

Statistical Modeling

Time Series

Supervised Learning

Unsupervised Learning

Data Visualization

Writing & Presentation

Data Wrangling

Communication

Education

MSc or PhD in applied mathematics or statistics

Tools

SQL

Spark SQL

Google/AWS/Azure

Job description

SteerBridge in Vienna, VA 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.

Requirements include hands-on experience with supervised learning (classification/regression) and unsupervised learning