Data Scientist - Supply Chain Analytics

Realign Llc; Seattle (WA); On-site; USD 120,000 - 160,000; Full time

Job summary

Realign Llc in Seattle, WA seeks a Data Scientist to lead supply chain analytics across MRO operations, building ML models and dashboards.

The role requires collaboration with stakeholders, strong cloud and data engineering skills, and the ability to operationalize analytics in a fast-paced environment.

This full-time onsite role offers opportunities to impact procurement and logistics through advanced analytics and GenAI capabilities.

Qualifications

• Proficiency in AWS services, AI/ML modeling, data modeling, data analytics, Tableau and Azure DevOps for project management.
• Strong proficiency in Python and/or other programming language.
• Experience with MRO process and supply chain data analysis.
• Experience with unstructured data processing and NLP.
• Experience with GenAI frameworks and applying analytics to business problems.
• Develop, test and validate ML models to predict future operations.
• Develop data models using AWS services (Sagemaker, Glue, Lambda, S3, Redshift).
• Publish model metrics and set up monitoring and alerts.

Responsibilities

• Collaborate with stakeholders to understand current MRO process flow.
• Gather actionable insights into maintenance and supply chain processes.
• Analyze data to optimize speed and quality of services.
• Incorporate models into a broader application driving actions by business and ops teams.
• Modeling & Advanced Analytics: algorithmic framework to process financial data and generate reports.
• NLP/GenAI Modeling: derive insights from unstructured data and notes.
• Develop project plan with milestones and deliverables; follow Agile.
• Develop, test and validate ML models; provide runbooks and documentation.

Skills

AWS services

AI/ML modeling

Data modeling

Data engineering

Data analytics

Tableau

Azure DevOps

Python

Tools

Sagemaker

Glue

Lambda

S3

Redshift

CloudWatch

SNS

Job description

Seattle, Washington 98039 Posted September 26th, 2026

Job Title: Data Scientist - Supply Chain Analytics

Location: Seattle, WA

Full Time

Job Description

Must Have Technical/Functional Skills

• Proficiency in AWS services, AI/ML modeling, Data modeling, data engineering, data analytics, tableau and Azure devops for project management.
• Strong Proficiency in Python and/or other programming language
• Should perform data analysis detailing the trends and bottlenecks in the MRO process and part supply chain.
• Experience with unstructured data processing and NLP
• Experience with generative-ai and agentic AI frameworks
• Experience in applying analytics in business problems
• Should develop, test, and validate the various machine learning models to predict for issues for future operations based on the historical data analysis from past operations.
• Development of data models using AWS services (e.g., Sagemaker, Glue, Lambda, S3, Redshift).
• Publish accuracy, precision, recall, F1-Score, MSE, R-squared etc. for the models
• Configuration of monitoring, logging, and alerting mechanisms (e.g., CloudWatch, SNS).
• Develop modular code that passes the static and dynamic Info-sec vulnerability scans
• Deploy automation to change the solution to be automated. E.g. Deployments, Certificate updates, Infrastructure changes, code changes, failure notifications etc.
• Document Runbook details of the above-mentioned models along with all the cloud and code assets created by the team.
• Conduct testing and validation activities for data and developed models.

Supply Chain Domain Knowledge

Strong grasp of supply chain processes, including inventory management, procurement and logistics.

Roles & Responsibilities

• Collaborate with stakeholders to understand the current MRO process flow
• Gather actionable insights into the process flow and supply chain for the maintenance, repair, and overhaul operations
• Analyze data around these processes and identify places where they can be optimized to provide quality services with greater speed.
• Incorporated models into a broader application which will drive actions by business and operations stakeholders
• Modeling & Advanced Analytics
  • o Algorithmic framework to process financial data and generate structured reports
  • o Validate accuracy of the generated reports against human written reports
• NLP/GenAI Modeling
  • o Algorithmic framework to process and derive insights from unstructured constraint notes data
  • o Identify data trends such as last time buyer updated the record and other informatio n to identify potentially stale, complete, cancelled and/or erroneous records
• Development of the project plan with key milestones and project deliverables
• Report out to stakeholders highlighting achievements, risks, and future work.
• Develop, test, and validate the various machine learning models
• Follow the Agile standard for the development of the requested proposal.
• Bring best practices, standards, and innovative ideas for Data Science, process, architecture, and design.
• Requirements gathering and architecture design.
• Development of data models using AWS services (e.g., Sagemaker, Glue, Lambda, S3, Redshift).
• Develop new Data Ingestion Patterns, use existing patterns/frameworks.
• Make data model outputs available for consumption, applications, and self-service.
• Build models that are performant and optimized for cloud expenses.
• Implement security, governance, monitoring, alerting, and job orchestration as defined by ARB.
• Conduct reviews along with frequent communication for stakeholders.
• Deployment of ingestion pipelines into dev, pre, and production environments.
• Configuration of monitoring, logging, and alerting mechanisms (e.g., CloudWatch, SNS).
• Unit testing, integration testing, functional, and non-functional testing.
• Handover documentation with a training session.

Generic Managerial Skills, If any

• Azure devops for project management
• Exceptional communication to bridge technical and non-technical teams.
• Strong analytical and problem-solving skills.
• Stakeholder management and cross-functional collaboration.