ML Data Scientist - Batteries

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Description

The Battery Analytics team transforms data into actionable insights by combining deep battery domain expertise with statistical, quantitative, and AI/ML methodologies.","responsibilities":"Analyze multi-source data (factory, laboratory, field, and failure data) and collaborate with cross-functional teams to resolve battery performance issues

Navigate Apple's database ecosystem to efficiently extract representative datasets while maintaining data quality standards for data-driven analysis

Design and implement robust data processing workflows; apply advanced modeling techniques with emphasis on causal analysis leveraging battery physics knowledge

Build comprehensive time-series and battery cycling data visualization and analytics capabilities

Conduct comparative studies, identify early warning signals, and lead failure analysis deep-dives

Drive resolution of anomalous forecasts for critical KPIs including battery swell, impedance, and capacity fade

Develop AI/ML capability for battery engineering leveraging state-of-the-art GenAI progress

Preferred Qualifications

Master's degree, PhD or equivalent job-related experience in Mechanical Engineering, Material Engineering, Electrical Engineering, Computer Science or relevant

Proven experience in statistical analysis, data mining, and causal inference methodologies

Advanced proficiency in big data processing, PyTorch, visualization and automated workflow

Hands-on experience with machine learning algorithms including ensemble methods, probabilistic networks, association rules, clustering, regression, neural networks, and large language mode

Strong analytical problem-solving abilities to address urgent ad-hoc requests by integrating engineering knowledge with advanced analytics and ML techniques across diverse data sources

Demonstrated expertise in anomaly detection techniques for time series and multivariate dataset

Self-motivated contributor who proactively collaborates across functions and develops innovative solutions beyond existing toolsets

Excellent communication skills with ability to explain complex technical concepts (particularly causal inference) to diverse audiences including data scientists, design engineers, and business stakeholders

Battery technology experience strongly preferred

Minimum Qualifications

BS degree in Mechanical Engineering, Material Engineering, Electrical Engineering, Computer Science or relevant

Experience with statistical analysis, data mining, or relevant

Experience using Python, R, or similar scripting languages

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant .

Pay & Benefits

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $147,400 and $272,100, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits.

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.