What You’ll Do Decode signals: Build data‑analysis pipelines to understand vehicle signals related to battery usage and machine signals related to battery manufacturing processes. Build battery‑health intelligence: Develop, validate, and maintain models that provide battery‑health assessments to customers of our connected battery products. Optimize manufacturing: Build and maintain models to increase production throughput, reduce scrap rates, and improve product quality. Collaborate & translate: Work with data scientists, engineers, and business stakeholders to craft solutions, and communicate the data‑driven decision process to non‑technical stakeholders. Support broadly: Provide ML and statistical solutions to other areas of the company as needed. What Success Looks Like Reliable battery‑health models in production, giving connected‑product customers assessments they can trust. Measurable manufacturing gains: higher throughput, lower scrap rates, and improved product quality driven by machine‑level data. Monitoring systems that surface meaningful signals and timely alerts to customers. Clear, well‑communicated insights that non‑technical stakeholders can act on. Core Competencies Strong statistical and ML foundations: hypothesis testing, design of experiments, regression, classification, clustering, and time‑series analysis. Hands‑on model development, training, validation, and deployment. Data storytelling and visualization for both analysis and model explanation. Curiosity, creativity, and self‑direction in a dynamic business environment. Effective collaboration across diverse, cross‑functional teams. Qualifications Required: BS in Statistics, Mathematics, Computer Science, or a related engineering field. 3+ years of experience, or equivalent academic experience with a master’s/PhD program in a relevant field. Proficiency programming with Python, Julia, or R and ML/statistics packages such as Scikit‑Learn, SciPy, Statsmodels, PyTorch, Keras. Experience with Power BI or Tableau. Good understanding of probability, statistics (hypothesis testing, design of experiments, power calculations, mixed‑effect models), linear algebra, and the mathematical bases of ML methods. Good understanding of ML techniques for supervised/unsupervised learning, feature selection, dimensionality reduction, regression, classification, clustering, and time‑series analysis. Experience interacting with databases and writing SQL queries. Experience developing, training, validating, and deploying ML/statistical models. Experience using data‑visualization techniques for analysis and model explanation. Experience collaborating effectively with non‑technical stakeholders; self‑driven, curious, and creative. Preferred: Experience with different deep learning architectures and how each applies to different problems. Preferred: Experience with big data technologies such as Databricks, Spark, Snowpark. Preferred: Experience with cloud technologies such as Microsoft Azure or AWS. Preferred: Experience deploying ML solutions for production (e.g., REST APIs in Azure ML, Docker, Kubernetes) in large data‑science projects. Preferred: Experience mentoring analysts and/or data scientists. Preferred: Causal inference, probabilistic graphical models, Bayesian statistics, and probabilistic programming languages/packages (Stan, PyMC, PyStan). Preferred: Experience in manufacturing, IoT, vehicle data, and/or battery engineering. Preferred: Publications in peer‑reviewed journals and/or conferences in statistics, mathematics, or machine learning. Equal Employment Opportunity: Clarios, LLC is an equal employment opportunity and affirmative action employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, protected veteran status, status as a qualified individual with a disability, or any other characteristic protected by law. For more information, please view EEO is the Law, EEO is the Law (supplement), and Pay Transparency Non‑discrimination. If you are an individual with a disability and you require an accommodation during the application process, please email #J-18808-Ljbffr