Senior Data Scientist

Pursuit Aerospace• US,

14d•$116,250 - $174,375•Remote

About The Position

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.

Requirements

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.

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
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

Responsibilities

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.
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.
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.