Data Scientist - Hardware Acoustics

Apple • Cupertino, CA

2d

About The Position

The Hardware Acoustic organization at Apple is dedicated to delivering
industry-leading audio experiences across all our products. We are a
multidisciplinary team of acoustic engineers, researchers, and machine learning
experts who push the boundaries of sound quality, noise cancellation, and user
interaction. Our Machine Learning Data team is the foundational backbone for
these efforts. We are responsible for building the robust data infrastructure,
pipelines, and analytical tools that enable the development, training, and
evaluation of cutting-edge machine learning models for acoustic applications. We
work with vast, complex datasets, ensuring their quality, accessibility, and
utility for our ML scientists and engineers.

DESCRIPTION

We are seeking a highly motivated and skilled Data Scientist/Engineer to join
our Machine Learning Data team within Hardware Acoustics. This role sits at the
intersection of data engineering, data science, and machine learning, with a
specific focus on acoustic and sensor data. You will be instrumental in
designing, developing, and maintaining scalable data pipelines, ensuring data
quality, and preparing complex datasets that power machine learning models
enhancing Apple's hardware acoustic performance. You will collaborate closely
with ML engineers, acoustic scientists, and hardware engineers to understand
their data needs and deliver impactful, data-driven solutions.

Requirements

• Bachelor's or Master's degree in Computer Science, Electrical Engineering, Data Science, or a related quantitative field.
• 3+ years of professional experience in data engineering, data science, or machine learning engineering roles, with a strong focus on data pipelines and data preparation.
• Expert proficiency in Python for data manipulation, scripting, and automation.
• Strong SQL skills for complex data querying, analysis, and database management.
• Experience with distributed data processing frameworks (e.g., Apache Spark, Hadoop).
• Solid understanding of data warehousing concepts, data modeling, and ETL/ELT principles.
• Experience with version control systems (e.g., Git).

Nice To Haves

• Experience working with acoustic data, audio signal processing, or sensor data.
• Familiarity with machine learning concepts and experience using ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Experience with MLOps principles and practices for managing the ML lifecycle.
• Knowledge of data visualization tools (e.g., Tableau, Power BI, matplotlib, seaborn).
• Experience with real-time data processing or streaming technologies.
• Excellent problem-solving, analytical, and communication skills, with the ability to explain complex data concepts to diverse audiences.
• Familiarity with web front/back end.
• Familiarity with large-scale data platforms and services (e.g., cloud-based data warehouses/lakes or similar internal infrastructure).

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level