Data Scientist at Amphenol in Valley Green, PA

Position: Data Scientist Location: Etters, PA Amphenol High Speed Products Group is the market leader for high-speed, high-bandwidth electrical connectors, cables, and systems for the Datacom/Telecom market (AI, ML, GPUs, Servers, Switches, Routers, Storage). Our products help to enable the artificial intelligence revolution by helping major Tier 1 Hyperscale Data Centers, their OEMs and break-out AI customers to innovate globally. Our global headquarters is in Nashua, NH and we have engineering, sales, and manufacturing locations globally. We are currently seeking a Data Scientist to join our engineering team. RESPONSIBILITIES: The Data Scientist will apply statistical modeling, machine learning, and advanced analytics to engineering data. Working alongside data analysts and software engineers, you will tackle complex, ambiguous problems and turn data into insight that drives engineering decisions. KEY RESPONSIBILITIES: Design and build statistical and machine learning models to solve engineering problems, such as predicting failures, optimizing processes, or identifying trends Partner with engineering stakeholders to translate open-ended questions into well-scoped analytical projects Explore and analyze large, complex datasets to uncover patterns and insights Collaborate with data analysts and software engineers to productionize models and integrate them into existing systems and workflows Validate model performance and monitor models in production, retraining or adjusting as needed Communicate findings, methodologies, and recommendations clearly to both technical and non-technical stakeholders Stay current on statistical and machine learning techniques and evaluate their applicability to engineering problems QUALIFICATIONS: A good candidate should have 3+ years of experience in a data science or applied statistics role. Bachelor's degree in Data Science, Statistics, Applied Mathematics, Computer Science, or a related field required. MS or PhD preferred. Solid understanding of statistical methods and machine learning techniques Comfort working with messy, real-world data, including cleaning, reconciling, and validating across multiple sources Strong communication skills, able to explain complex methodologies to non-technical stakeholders TECHNICAL EXPERTISE: Strong proficiency in Python (scikit-learn, pandas, numpy, or similar) for model development Experience with SQL for querying and preparing data