Job Description

Amphenol is one of the largest manufacturers of interconnect products in the world. Founded in 1932, Amphenol designs, manufactures and markets electrical, electronic and fiber optic connectors, interconnect systems, antennas, sensors and sensor-based products and coaxial and high-speed specialty cable. Amphenol has been on the forefront of enabling the electronics revolution by driving value through innovation and connecting people and technology. The primary end markets for the company's products are communications and information processing markets, including cable television, cellular telephone and data communication and information processing systems; aerospace and military electronics; and automotive, rail and other transportation and industrial applications. Listed on the NYSE with sales of $15.2 Billion in 2024 and a Fortune 500 company, Amphenol is a top performing company with an unparalleled history of growth. With over 125,000 dedicated employees working in 150 businesses in more than 40 countries, Amphenol has the unique advantage of having both diversified global reach while still being a focused organization. Our focus comes from our entrepreneurial management teams dedicated to specific markets and regions.

Job Title: Manager - Data Analytics and AI Engineering

Location: United States

Department: Internal Audit Department

Reports to: Associate Director - Data Analytics and Transformation

Job Description:

Overview

We are seeking a highly motivated Data Analytics & AI Engineering Manager to join our Internal Audit department. This role will play a critical part in modernizing and streamlining our audit processes through data and ML pipelines, production deployment of machine learning models, and development of LLM-based retrieval and multi-agent solutions.

Key Responsibilities

• Design and implement scalable analytics and ML pipelines on Databricks for audit use cases (e.g., journal entry analysis, control testing, PBCs).
• Develop, validate and productionize ML models for anomaly detection, classification, risk scoring, and predictive analytics; ensure reproducibility, versioning and monitoring.
• Architect and deliver secure RAG (retrieval-augmented generation) solutions and vector-based retrieval layers for sensitive audit documents.
• Design and implement LLM-driven applications and multi-agent workflows to automate triage, evidence aggregation, summarization and related audit tasks.
• Define and maintain data product contracts; collaborate with data platform and integration teams to ensure datasets are fit for model training and serving.
• Implement model governance controls including validation, bias detection, explainability, drift monitoring and performance tracking.
• Produce operational dashboards and visualizations (Power BI / Tableau) that translate model outputs into actionable audit information.

Document designs, deliver technical handoffs and conduct knowledge transfers with audit stakeholders while ensuring traceability and auditability of automated solutions.

Qualifications:

Experience and Skills:

• Bachelor’s Degree in data Analytics, Computer Science, Information Systems, Accounting Information Systems, or a related field.
• Minimum 5 years’ professional experience in data science, ML engineering or applied analytics with production deployments.
• Proficiency in Python (including pandas, numpy) and common ML frameworks (scikit-learn, PyTorch or TensorFlow).
• Experience with experiment and metadata tracking tools (MLflow or equivalent).
• Demonstrated experience building LLM-based applications (RAG pipelines, embeddings/vector search, prompt engineering).
• Familiarity with agent orchestration concepts or frameworks for coordinating multiple models/tools.
• Strong SQL and data modeling skills; ability to translate audit requirements into analytic designs.
• Experience operationalizing models (serving, monitoring, drift detection, retraining workflows).

Desirable Skills:

• Experience in internal audit, risk management, or compliance functions.
• Practical experience with Databricks / Spark (notebooks, jobs, ML pipelines).
• Familiarity with audit testing methodologies or audit management software.
• Experience with data visualization tools (Power BI, Tableau, Qlik, etc.).
• Exposure to ETL tools, APIs, or cloud-based data platforms.
• Knowledge of process mining or continuous auditing techniques.

Soft Skills:

• Effective collaboration and communication skills, with a proven ability to work with interdisciplinary teams.
• Strategic thinker with a proactive approach to fraud risk assessment and process optimization.
• Commitment to upholding data integrity, confidentiality, and ethical standards in all forensic and data-driven activities.

Travel:

• Up to 25% travel

Amphenol Corporation is an equal opportunity employer and we encourage applications from all qualified candidates.

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