Senior Data Scientist – Aerospace Analytics (Onsite Vienna)

SteerBridge; Vienna (VA); Hybrid; USD 160,000 - 180,000; Full time

Benefits offered by this job

Health insurance

Vision insurance

Life Insurance

401(k) Retirement Plan with matching

Paid Time Off

Job summary

SteerBridge is seeking a Senior Data Scientist to evaluate multi-dimensional aviation global supply chain and operational data to construct and maintain predictive models. Candidates must be familiar with generalized linear and multilinear regression, logistic and multinomial regression, and time series analysis.

Candidates must have hands-on experience with supervised (classification, regression) and unsupervised learning.

Qualifications

• M.S. or Ph.D. in Applied Mathematics, Statistics, or a related field; or a Bachelor's with equivalent experience.
• Active security clearance or ability to obtain one.
• 6+ years in aerospace preferred.
• Hands-on experience with supervised (classification/regression) and unsupervised learning.
• Experience documenting algorithm development and implementation.

Responsibilities

• Evaluate multi-dimensional aviation data to build predictive models.
• Lead end-to-end analytics and data science framing activities.
• Collaborate with stakeholders to identify data fields, provenance and integration.
• Develop data quality assurance and documentation for data processes and models.
• Review large datasets to identify relevant trends and patterns.
• Work on-site in Vienna, VA (3+ days/week) with hybrid options as per supervisor.

Skills

R or Python

SQL or Spark SQL

Time series analysis

Classification & Regression

Data wrangling

Data visualization

Documentation

Communication

Cloud platforms

On-site collaboration

Security clearance

Education

MS/PhD in Applied Math/Statistics or related

Tools

Python

R

SQL

Spark

AWS/GCP/Azure

Job description

SteerBridge is seeking a Senior Data Scientist to evaluate multi-dimensional aviation global supply chain and operational data to construct and maintain predictive models. Candidates must be familiar with generalized linear and multilinear regression, logistic and multinomial regression, and time series analysis.

Candidates must have hands-on experience with supervised (classification, regression) and unsupervised learning.