Do you have a passion for invention and self-challenge? Do you thrive on pushing the limits of what’s considered feasible? As part of our Battery Engineering group, you’ll help craft creative battery solutions that deliver more energy in smaller spaces than ever before! We work across subject areas to transform improved hardware elements into a single, integrated design. Join us, and you’ll help us innovate new battery technologies that continually outperform the previous iterations. By collaborating with other product development groups across Apple, we push the industry boundaries of what batteries can do and improve the product experience for our customers across the world! This position works within a multi-functional team supporting the entire Battery Department to identify trends, mine data, and help the battery team improve performance.
Proven working knowledge of statistical and data mining for causal analysis.
Strong expertise in processing and visualizing and analyzing big data as well as automating processes using a scripting language (Python, R, etc.).
Solid understanding of Python, JMP, SQL, and Tableau.
Experience in machine learning algorithms including ensemble-based approaches, probability networks, association rules, clustering, regression, and neural networks.
Strong problem-solving skills to address ad-hoc analytics requests with a sense of urgency by using engineering knowledge and analytics/ML skills based on different data sources, including time series dataset.
Experience in anomaly detection based on time series or other datasets.
Self-starter to interact with other functions, shown creativity to go beyond current tools to deliver the best solution to the problem.
Effective interpersonal skills to articulate difficult technical topics, especially causal inference, to collaborators including peer data scientists, design engineers, and business partners.
Strong analytics, statistical, and ML skills. Battery experience is preferred.
The goal of the Battery Analytics team is to turn data into useful insights using battery knowledge as well as statistical, quantitative, and machine learning technologies.
- Analyze factory, field, and failure data and use engineering understanding to collaborate with cross-functional teams to resolve battery issues.
- Quick study to understand end to end nature of Apple DB to organize proper sample size, proper data pull, and minimize SQA issues with maximum efficiency.
- Design, implement, process and workflow and interpret data; Apply advanced technologies, for modeling enhancements, especially to drive causation with extraordinary battery mechanistic expertise.
- Develop time-series data and cycling data visualization and analytics capability, conduct comparative studies, identify early signals, and lead deep dive for FA.
- Lead resolution of out-of-family forecasts for KPIs such as swell, impedance, and capacity.
- Solid understanding of Python, JMP, SQL, and Tableau.
- Battery Engineering expertise, analytics, statistical, and ML skills.
BS plus 3 years of relevant industry experience. Advanced degree preferred.