Data Scientist - Supply Chain Analytics

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 information 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.