Senior Data Scientist at Pursuit Aerospace

Job description

About Us: As a global manufacturer of complex aircraft engine components, Pursuit Aerospace is founded on a commitment to relentless, continuous, operational improvement and extraordinary customer service. We pride ourselves on competitive cost structure, exceptional on-time delivery, and industry-leading quality. Pursuit Aerospace cultivates long-term relationships with our customers around the world through respect, teamwork, technology, and trust. We are driven to develop industry leading process innovations and manufacturing techniques on behalf of our customers. About the Opportunity: The senior data scientist will build data, analytics, and AI solutions to support SIOP (Sales, Inventory, Operations Planning) transformation and delivery business impact on inventory and customer service. The senior data scientist will function like a forward-deployed engineer who takes a supply chain problem from ambiguity to financial impact: framing the problem, shaping the data and models, building and iterating fast, and owing the solution end to end. This role will work shoulder-to-shoulder with the business, move at its pace, and turn large, messy operational data into robust models, dashboards, and AI-powered tools that change decisions and outcomes. Location: Remote in the U.S., and travel 10-20%. Responsibilities: Data Science & Machine Learning • Develop and produce machine learning, statistical, forecasting, and time-series models that solve complex operational and business problems. • Build solutions supporting key business areas including inventory optimization, demand forecasting, clear-to-build analytics, on-time delivery, production planning, operational efficiency, and cost reduction. • Perform exploratory analysis, feature engineering, model selection, validation, optimization, deployment, and ongoing performance monitoring. • Develop predictive and prescriptive analytical solutions that translate large and complex datasets into measurable business outcomes. • Develop AI-powered applications and tools, including Generative AI and Large Language Model solutions where they provide measurable business value. End-to-End Data Science Products • Independently build end-to-end data science products, from business problem definition and data preparation through modeling, application development, deployment, and adoption. • Develop robust analytical datasets and transformation workflows using Python and SQL to support machine learning and advanced analytics. • Work with large, complex datasets across multiple enterprise systems and independently identify, clean, transform, and integrate the data required for analytical solutions. • Develop dashboards, analytical applications, APIs, or decision-support tools that enable business users to consume model outputs and analytical insights. • Build production-quality solutions with appropriate testing, version control, monitoring, documentation, and maintainability. Data & Technology Collaboration • Leverage modern cloud and data technologies such as AWS, Snowflake, Spark, S3, and related services to develop scalable analytical and machine learning solutions. • Understand modern data architecture concepts including data lakes, ETL/ELT, streaming data, APIs, and cloud-native data platforms sufficiently to design data science solutions that integrate effectively with enterprise platforms. • Collaborate with Product, Engineering, Operations, Supply Chain, and business stakeholders to translate business capabilities and requirements into practical analytical solutions. • Deliver high-impact analytics: partner with cross-functional leaders to scope the problems and ship solutions that move real metrics: inventory, on-time delivery, and cost. • Build end-to-end products: build products independently and iterate quickly based on business feedback including data pipeline and snowflake tables, forecasting and time-series models, inventory and clear-to-build analytics, dashboards, and AI-powered tools. • Run analytics assets in production: keep all assets accurate and reliable and manage analytics operations leveraging technology • Engineer trustworthy data: turn large, messy operational data into well-structured, trusted datasets, and develop continuous improvement framework to enhance data quality and integrity • Drive decisions and adoption: translate analytical results and tools into clear, decision-ready recommendations and drive adoption across the business Required Qualifications: • Bachelor’s degree • 4 + years in a data science role demonstrating solving complex business problems and independently building robust solutions that improve outcomes • 4 + years of hands-on experience using SQL and Python for data analysis, data transformation, and analytical solution development. • 4 + years of experience developing machine learning, demand forecasting, or time-series models. • Must be authorized to work in the U.S. on a full-time basis without sponsorship now or in the future. The Company cannot offer employment to visa holders who require employer sponsorship in the future or cannot work now on a full-time basis. • Must be able to perform work subject to ITAR/EAR regulations. Preferred Qualifications: • Bachelor’s degree in data science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field. • Master’s degree in a quantitative or technical field. • Proficient in SQL, Python, ML modeling, and time-series analytics; hands-on with Codex for AI-assisted coding against Snowflake tables, pipeline orchestration, GitHub for CI/CD, Azure data platform • Demonstrated experience independently developing and deploying machine learning, statistical, or time-series forecasting models, including end-to-end ownership from problem formulation and data preparation through model validation, deployment, and monitoring. Experience • Experience independently working with large, complex, and imperfect datasets, including data exploration, transformation, feature engineering, data quality remediation, and development of production-ready analytical datasets. • Experience designing and implementing data models and ETL/ELT pipelines in modern cloud data environments such as Snowflake, AWS, Azure, or GCP. • Experience developing production-quality analytical or machine learning solutions using testing, version control, CI/CD, monitoring, and documentation. • Experience with AI-assisted product development and using AI tools to plan, build, quality-check, debug, and improve code. • Supply chain analytics knowledge such as demand forecasting, demand sensing, inventory management and clear-to-build reporting • Aerospace and/or manufacturing experience • Familiarity with ERP systems and hands-on experience with purchase order, production order, and inventory data Working Conditions: • Requires mobility in a manufacturing plant environment while using Personal Protective Equipment. • Must be able to frequently sit, stand and walk. • Must be able to lift and carry up to 15 pounds. • Must be able to have prolonged periods sitting at a desk and working on a computer.Must be able to travel between locations or to suppliers up to 25% of time. Acknowledgements: The above job description is not intended to be an all-inclusive list of duties and standards of the position. Incumbents will follow any other instructions, and perform any other related duties, as assigned by their supervisor. Benefits: Pursuit Aerospace also offers a variety of benefits, including health and disability insurance, 401(k) match, flexible spending accounts, EAP, paid time off, and company-paid holidays. The specific programs and options available to an employee may vary depending on date of hire, schedule type, and the applicability of collective bargaining agreements, among other things. Equal Opportunity Employer: Pursuit Aerospace is an Equal Opportunity Employer. We adhere to all applicable federal, state, and local laws governing nondiscrimination in employment. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.

Requirements

Must-haves

Remote in the U.S., and travel 10-20%.
Data Science & Machine Learning
Develop and produce machine learning, statistical, forecasting, and time-series models that solve complex operational and business problems.
Build solutions supporting key business areas including inventory optimization, demand forecasting, clear-to-build analytics, on-time delivery, production planning, operational efficiency, and cost reduction.
Perform exploratory analysis, feature engineering, model selection, validation, optimization, deployment, and ongoing performance monitoring.
Develop predictive and prescriptive analytical solutions that translate large and complex datasets into measurable business outcomes.
Develop AI-powered applications and tools, including Generative AI and Large Language Model solutions where they provide measurable business value.
End-to-End Data Science Products
Independently build end-to-end data science products, from business problem definition and data preparation through modeling, application development, deployment, and adoption.
Develop robust analytical datasets and transformation workflows using Python and SQL to support machine learning and advanced analytics.
Work with large, complex datasets across multiple enterprise systems and independently identify, clean, transform, and integrate the data required for analytical solutions.
Develop dashboards, analytical applications, APIs, or decision-support tools that enable business users to consume model outputs and analytical insights.
Build production-quality solutions with appropriate testing, version control, monitoring, documentation, and maintainability.
Data & Technology Collaboration
Leverage modern cloud and data technologies such as AWS, Snowflake, Spark, S3, and related services to develop scalable analytical and machine learning solutions.

Nice-to-haves

Bachelor’s degree in data science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
Master’s degree in a quantitative or technical field.
Proficient in SQL, Python, ML modeling, and time-series analytics; hands-on with Codex for AI-assisted coding against Snowflake tables, pipeline orchestration, GitHub for CI/CD, Azure data platform
Experience designing and implementing data models and ETL/ELT pipelines in modern cloud data environments such as Snowflake, AWS, Azure, or GCP.
Experience developing production-quality analytical or machine learning solutions using testing, version control, CI/CD, monitoring, and documentation.
Experience with AI-assisted product development and using AI tools to plan, build, quality-check, debug, and improve code.
Supply chain analytics knowledge such as demand forecasting, demand sensing, inventory management and clear-to-build reporting
Aerospace and/or manufacturing experience
Familiarity with ERP systems and hands-on experience with purchase order, production order, and inventory data

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningpythonsqlawsmlci/cdazuregcp

Platform: adp-workforce-now
Effort: medium

Benefits

Benefits detected in the listing.

401(K)

Paid Time Off