Data Scientist
Foxconn
full-time
Posted on:
Location Type: Office
Location: Milwaukee • Wisconsin • United States
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About the role
- Design, build and maintain end-to-end data science and GenAI solutions for manufacturing use cases, from problem framing and data exploration through model development, validation, deployment and monitoring.
- Develop and operationalize GenAI and LLM-based applications for industrial scenarios, such as: Natural-language copilots for process engineers and operators to query production, quality and equipment data; Work-instruction and SOP assistants that summarize and personalize procedures by product, line or station; Root-cause and anomaly analysis assistants that explain out-of-control conditions and suggest likely causes; NL-driven maintenance and quality-report generators that transform logs and sensor data into clear narratives.
- Implement natural-language–based data analytics and data modeling workflows that convert user questions into reproducible analyses (e.g., auto-generated SQL / queries, model runs, dashboards) and return interpretable results aligned with KPIs such as yield, scrap, downtime and OEE.
- Work with structured and unstructured data from diverse factory and enterprise sources (MES, ERP, sensor/IoT data, images, PDFs and logs), including data ingestion, cleaning, feature engineering and data quality checks.
- Build and refine GenAI workflows including prompt design, retrieval-augmented generation (RAG), vector search, tool-calling and guardrails, with a focus on safety, reliability, latency and explainability in production settings.
- Collaborate with software engineers and architects to integrate ML and GenAI models into backend services and the Smart Manufacturing Platform, supporting API design, performance tuning and lifecycle management.
- Participate in planning and roadmap discussions for AI initiatives: help define scope, technical approach, success metrics, data requirements and deployment plans across multiple plants and product lines.
- Create clear documentation, technical reports and reusable code artifacts so that solutions can be supported, scaled, and transferred to other teams or sites Engage with cross-functional stakeholders (manufacturing, operations, quality, IT, business teams) to understand needs, prioritize opportunities and present findings and recommendations to both technical and non-technical audiences.
- Stay current with advancements in data science, machine learning and GenAI (models, frameworks, tooling, MLOps/LLMOps) and share best practices within the team.
- Perform other duties and responsibilities as required or requested.
Requirements
- Bachelor’s degree in Computer Science, Data Science, Statistics, Engineering or a related field required; Master’s degree preferred.
- 2–5 years of recent, hands-on experience in data science and machine learning, including delivering models or analytics solutions into production or business use.
- Strong foundation in core ML techniques (regression, classification, clustering, time-series, deep learning; supervised and unsupervised methods) and experience applying them to real business problems.
- Practical experience with generative AI and large language models, such as prompt engineering, fine-tuning or adapting foundation models, building chat/assistant workflows, or RAG-style applications.
- Proficiency in Python for data science and ML (e.g., pandas, NumPy, scikit-learn; plus PyTorch or equivalent).
- Solid understanding of data engineering and storage technologies, including SQL and NoSQL databases and data pipeline/ETL concepts.
- Experience with big data frameworks or streaming data is a plus.
- Familiarity with modern software development practices (Git, code review, testing, CI/CD) and with web/API concepts (RESTful services, basic HTML/CSS/JavaScript) is preferred.
- Experience working with cloud platforms and, ideally, GPU-accelerated environments for deploying AI models, is a plus.
- Strong analytical and problem-solving skills, with the ability to design experiments, interpret complex results, and drive decisions using data.
- Team-oriented mindset and demonstrated ability to collaborate with distributed, cross-functional teams, with excellent written and verbal communication skills.
Benefits
- Equal employment opportunities (EEO)
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
data sciencemachine learninggenerative AIlarge language modelsprompt engineeringPythonSQLNoSQLdata engineeringETL
Soft skills
analytical skillsproblem-solving skillscollaborationcommunication skillsteam-oriented mindset