Data Scientist - Supply Chain Analytics

About the Role

This position focuses on leveraging advanced analytics, AI/ML modeling, and data engineering to optimize Maintenance, Repair, and Overhaul (MRO) processes and supply chain operations. The role involves developing predictive models, processing unstructured data, and implementing automation to drive business improvements.

Responsibilities

• Collaborate with stakeholders to understand the current MRO process flow and gather actionable insights.
• Analyze data to identify trends, bottlenecks, and optimization opportunities in supply chain processes.
• Develop, test, and validate machine learning models to predict operational issues based on historical data.
• Publish model performance metrics such as accuracy, precision, recall, F1-Score, MSE, and R-squared.
• Develop data models using AWS services (e.g., Sagemaker, Glue, Lambda, S3, Redshift).
• Configure monitoring, logging, and alerting mechanisms (e.g., CloudWatch, SNS).
• Deploy automation for solution changes, including deployments, certificate updates, infrastructure changes, and failure notifications.
• Document runbooks for models and cloud/code assets created by the team.
• Conduct testing and validation activities for data and developed models.
• Incorporate models into broader applications to drive actions by business and operations stakeholders.
• Develop algorithmic frameworks to process financial data and generate structured reports, validating accuracy against human-written reports.
• Process and derive insights from unstructured constraint notes data using NLP/GenAI, identifying trends and potentially stale or erroneous records.
• Develop project plans with key milestones and deliverables, reporting progress, risks, and future work to stakeholders.
• Follow Agile standards for development and bring best practices in data science, process, architecture, and design.
• Gather requirements and design architecture for data solutions.
• Develop new data ingestion patterns and optimize existing frameworks.
• Make data model outputs available for consumption, applications, and self-service.
• Build performant models optimized for cloud expenses.
• Implement security, governance, monitoring, alerting, and job orchestration as defined by ARB.
• Conduct reviews and maintain frequent communication with stakeholders.
• Deploy ingestion pipelines into dev, pre-production, and production environments.
• Perform unit testing, integration testing, functional, and non-functional testing.
• Provide handover documentation and training sessions.

Requirements

• Proficiency in AWS services, AI/ML modeling, data modeling, data engineering, and data analytics.
• Strong proficiency in Python and/or other programming languages.
• Experience with unstructured data processing and NLP.
• Experience with generative AI and agentic AI frameworks.
• Experience applying analytics to business problems.
• Development of data models using AWS services (e.g., Sagemaker, Glue, Lambda, S3, Redshift).
• Strong grasp of supply chain processes, including inventory management, procurement, and logistics.
• Exceptional communication skills to bridge technical and non-technical teams.
• Strong analytical and problem-solving skills.
• Stakeholder management and cross-functional collaboration.
• Experience with Azure DevOps for project management.

Qualifications

Bachelor of Computer Science or equivalent.

Skills

• AWS services (Sagemaker, Glue, Lambda, S3, Redshift, CloudWatch, SNS)
• Python and other programming languages
• AI/ML modeling and data engineering
• Data analytics and Tableau
• Unstructured data processing and NLP
• Generative AI and agentic AI frameworks
• Agile development methodologies
• Azure DevOps for project management
• Supply chain domain knowledge (MRO, inventory management, procurement, logistics)

Pay

Base salary range: $135,000 to $140,000 per annum.

Benefits

• Discretionary annual incentive.
• Comprehensive medical coverage (Medical & Health, Dental & Vision).
• Disability planning & insurance, pet insurance plans.
• Family support (maternal & parental leaves).
• Insurance options (auto & home insurance, identity theft protection).
• Commuter benefits & certification & training reimbursement.
• Paid time off (vacation, sick leave, holidays).
• Legal & financial assistance (legal assistance, 401K plan, performance bonus, college fund, student loan refinancing).