Data Scientist

Air is the leader in Enterprise Readiness. Our mission is to establish readiness as a real-time condition that is continuously achieved. Today, a dangerous Readiness Gap exists between what the front line needs and what is delivered. Our AI-native platform, Air Enterprise Readiness, aligns development, production, delivery, and sustainment into one coordinated execution system for government agencies and industrial suppliers. By revealing true capacity, exposing real constraints, coordinating resources, and executing at the speed of operational demands, the front line gets what it needs to succeed.

About the role

We are seeking an inquisitive data scientist to join our team and work with our various datasets to find connections, knowledge, and potential issues in order to help our government clients make more informed decisions. Your role on the Data Science team will be primarily focused on our large datasets, where you will design and implement scalable statistical systems based on your analysis. You will have the opportunity to expand and grow data-driven research across Air, and lead new areas to apply advanced analytics to drive business results.

This role is a full-time position located out of our office in Pittsburgh, PA and may require up to 25% travel.

Responsibilities

• Design experiments, test hypotheses, and build models for advanced data analysis and complex algorithms
• Apply advanced statistical and predictive modeling techniques to build, maintain, and improve multiple real-time decision systems
• Make strategic recommendations on data collection, integration, and retention requirements, incorporating business requirements and knowledge of data industry best practices
• Model and frame business scenarios that are meaningful and which impact critical processes and decisions; transform, standardize, and integrate datasets for client use cases
• Convert custom, complex and manual client data analysis tasks into repeatable, configurable processes for consistent and scalable use within the Air SaaS platform
• Optimize processes for maximum speed, performance, and accuracy; craft clean, testable, and maintainable code
• Partner with internal Air business analysts and external client teams to seek out the best solutions regarding data-driven problem-solving
• Participate in end-to-end software development, on an agile team in a scrum process, collaborating closely with fellow software, machine learning, data, and QA engineers

Requirements

• U.S. Citizenship is required
• Bachelor's degree in Computer Science, Computer Engineering, Mathematics, Statistics, or a related field; Master’s or PhD preferred
• Minimum 3 years of hands-on data science experience
• Minimum 3 years of experience deriving key insights and KPIs for external and internal customers
• Regular development experience in Python
• Prior hands-on experience working with data-driven analytics
• Proven ability to develop solutions to loosely defined business problems by leveraging pattern detection over large datasets
• Proficiency in statistical analysis, quantitative analytics, forecasting/predictive analytics, multivariate testing, and optimization algorithms
• Experience using machine learning algorithms (e.g., gradient-boosted machines, neural networks)
• Ability to work independently with little supervision
• Strong communication and interpersonal skills
• A burning desire to work in a challenging fast-paced environment

Skills

• Current possession of a U.S. security clearance, or the ability to obtain one with our sponsorship
• Experience in or exposure to the nuances of a startup or other entrepreneurial environment
• Experience working on agile/scrum teams
• Experience building datasets from common database tools using flavors of SQL
• Expertise with automation and streaming data
• Experience with major NLP frameworks (spaCy, fasttext, BERT)
• Familiarity with big data frameworks (e.g., HDFS, Spark) and AWS
• Familiarity with Git source control management
• Experience working in a product organization
• Experience analyzing financial, supply chain/logistics, or intellectual property data