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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Science Engineer - **Company:** Molex - **Location:** Lisle, IL, United States - **Salary:** $62,400.0 - **Contract:** Internship / Graduate position - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Profiling, Computer Programming, Continuous Integration, Information Engineering, Data Mining, Data Structures, Decision Support Systems, Python (Programming Language), NumPy, Rapid Prototyping Process, Cloud Services, Software Construction, Software Engineering, SQL Databases, Data Streaming, Management of Software Versions, Feature Engineering, Data Ingestion, Deep Learning, Model Validation, Pandas, Event Driven Architecture, Kubernetes, Information Technology, Production Code, Data Management, Machine Learning Operations, Docker - **Published:** June 4, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=ae252d8f4e554400 ## About the Role Do you have experience in Software engineering?, * Working in School to reach a Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Industrial Engineering, Operations Research, Applied Math, or related field. * Strong programming skills (such as Python, SQL, etc.). * Solid computer science fundamentals (data structures, algorithms, software design) and disciplined software engineering practices. What Will Put You Ahead * Internship or project experience in manufacturing, supply chain, inventory planning, or factory scheduling. * Experience using agentic coding tools and AI-assisted development workflows is a plus. * Experience with MLOps tools and CI/CD for ML (e.g., MLflow, Kubeflow, or comparable tooling). * Experience building developer tooling, templates, or internal platforms. * Portfolio of data science and software projects demonstrating deployments, services, or integrated solutions. * Familiarity with container orchestration, cloud services, and event-driven systems. Team, Culture & PBM * Quick learner who enjoys domain learning - either already familiar with narrow business domains (supply chain, manufacturing) or eager to immerse and learn. * Strong communicator able to translate technical results into business impact. * Systems thinker: able to understand, diagnose, and redesign systems and end-to-end data flows (process engineering mindset). * Comfortable with statistical, optimization, and deep learning concepts and able to learn the practical application for business problems. * Small, high-velocity team that values rapid prototyping, pragmatic engineering, and close partnership with domain SMEs. ## Description Location/Work Arrangements: Lisle, IL - Summer Intern - Onsite 4-5 Days Per Week., * Build and productionize AI projects with the One Molex Team to optimization solutions that drive measurable business outcomes. * Translate supply chain and manufacturing problems into measurable ML/optimization problem statements and success metrics. * Prototype, validate, and harden models using appropriate statistical, optimization, and deep learning approaches; ensure models are robust, explainable, and uncertainty-aware. * Implement model-management best practices: versioning, CI/CD for models, automated training pipelines, validation suites, monitoring, drift detection, and retraining triggers. * Work closely with data engineering and platform teams to ensure high-quality data, feature stores, and scalable inference. * Instrument solutions with business-facing reporting and dashboards to enable adoption and continuous improvement. * Build production-quality code and services: package models as APIs/services, containerize for deployment, and integrate with pipelines and orchestration. * Be capable and willing to create full-stack software solutions as needed - from data ingestion and feature pipelines to model serving and user-facing reports. * Support optimization and decision models used for SCOE Planning, MFG Planning, SCH Hub operations & inventory policy evaluation, and multi-node supply decisions; translate mathematical formulations into maintainable code and services. * Collaborate with other data scientists across Molex businesses, capability teams, and our India Technology Center as needed - building integrated solutions from the Central "Hub" team that are delivered and adopted by the business "Spokes." Role Mapping * Data Science Operations: MLOps pipelines, telemetry, model performance dashboards. * Data Management & Analysis: source, cleanse, and transform time-series and master data; feature engineering. * Research & Advanced Processing: prototype and evaluate advanced modeling and optimization methods. * Data-driven Decision Making: deliver models that measurably improve KPIs and inform operational decisions. Core Technical Skills & Tools * Programming: Python (pandas, NumPy), production code hygiene, testing, and software engineering best practices. * SQL for data extraction, profiling, and validation. * Familiarity with statistical modeling, optimization methods, and deep learning modeling concepts - emphasis on strong fundamentals and selecting the right approach for the problem. * MLOps fundamentals: model packaging, automated training and deployment (CI/CD), containerization (Docker), model monitoring and alerting. * Ability to design and implement end-to-end solutions: data ingestion, feature engineering, model training, serving, and reporting. * Communication: ability to present model outcomes, assumptions, and business impact to stakeholders in plain language. Skills & Domain Knowledge (from Demand Planning materials, at a high level) * Demand Forecasting: framing forecasting problems, producing probabilistic forecasts, and evaluating the impact of uncertainty on decisions. * Safety Stock & Inventory Concepts: service-level definitions (e.g., Cycle Service Level / Fill Rate), probability-based buffers, and multi-echelon inventory considerations. * Multi-Echelon & Inventory Optimization: understanding stochastic approaches, policy evaluation, and trade-offs between cost, service, and obsolescence. * Planning & Scheduling / Factory Optimization: familiarity with formulating and evaluating network flow, LP/MILP, and stochastic optimization models in manufacturing contexts. * Business Metrics Orientation: focus on improving inventory turns, DSI, fill rates/service level, working capital, and supply risk exposure. ## Related Videos - [Vectorize all the things! 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