Data Scientist - Supply Chain Analytics

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.