• Design, develop, and deploy scalable data science, machine learning, and AI solutions that drive business value • Build production-ready ML pipelines, from feature engineering and model development through deployment and monitoring • Develop AI-powered applications using modern frameworks such as Streamlit and integrate Large Language Models (LLMs) into business workflows • Design and maintain Retrieval-Augmented Generation (RAG) solutions, knowledge bases, and AI agents using structured and unstructured enterprise data • Perform quantitative and qualitative analyses to identify trends, generate insights, and support strategic decision-making • Partner with business stakeholders to translate complex business challenges into scalable AI and analytics solutions • Collaborate with data engineers, software engineers, analysts, and product teams on cross-functional initiatives • Improve existing data science processes, identify automation opportunities, and promote engineering best practices • Contribute to technical documentation, knowledge sharing, and continuous improvement initiatives

Role Description The lead 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 lead 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 - Owning 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. Responsibilities - 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. Qualifications - Bachelor’s degree required - 2+ years in a data science role demonstrating solving complex business problems and independently building robust solutions that improve outcomes - 2+ years in SQL, Python, Machine learning modeling, and time-series analytics - Must be authorized to work in the U.S. on a full-time basis without sponsorship now or in the future - Must be able to perform work subject to ITAR/EAR regulations Preferred Qualifications - Bachelor’s degree in data science or related field preferred - Master’s degree preferred - Proficient in SQL, Python, ML modeling, and time-series analytics; hands-on with Claude for AI-assisted coding against Snowflake tables, pipeline orchestration, GitHub for CI/CD, Azure data platform - Effective AI-assisted product development and proficient in using AI to plan, build, and quality-check 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. Benefits - Health and disability insurance - 401(k) match - Flexible spending accounts - EAP - Paid time off - Company-paid holidays 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.

• Build real-time machine learning models utilizing very large datasets to deliver scalable solutions to customer problems • Partner with product managers, engineers, and designers to build and iterate on data-driven features • Drive data exploration and experimentation to identify new product opportunities using Amazon and e-commerce behavioral data • Design and run experiments to evaluate the impact of optimizations and communicate results to stakeholders • Establish scalable, efficient, and automated processes for large-scale data analysis, model development, and inference • Build scalable pipelines using Python, SQL, and AWS to operationalize insights • Define metrics, create dashboards, and monitor model performance in production • Champion best practices in model development, testing, and monitoring • Write production-ready code

• Lead the end-to-end data science project lifecycle, from understanding the business pain point to deploying and monitoring models in production • Develop and enhance Machine Learning models (supervised and unsupervised) applied to recruitment, selection, retention, and engagement • Perform advanced exploratory analyses to identify patterns and hidden opportunities in user behavior • Collaborate closely with Product and Engineering teams to integrate data solutions directly into the platform • Ensure model quality and governance, focusing on bias mitigation and explainability (XAI) • Mentor junior team members, promoting the dissemination of best practices in coding, experimentation, and scientific rigor • Communicate technical findings clearly and strategically to stakeholders across different areas of the company.