Machine Learning Data Scientist Needed at Apple

Location: Austin, Texas, United States

Industry: Support and Service
Role Number: 200534530

At Apple, we believe in turning new ideas into extraordinary products, services, and customer experiences. Join the AppleCare Business Insights, Operations, and Systems (ACBIOS) team as a Hardware Insights Machine Learning Data Scientist. We are seeking an experienced professional who is passionate about using analytics to drive business decisions and enhance customer experiences. If you have a proven track record in deploying data science and machine learning techniques, uncovering insights, and translating business questions into data solutions, we invite you to bring your dedication and passion to our team.

Key Qualifications:

Over 5 years of experience deploying data science, machine learning, and anomaly detection techniques to solve practical business problems.
Expert proficiency in SQL, preferably in Snowflake, with strong debugging skills and a consistent record of developing clean, efficient code.
Experience with anomaly and outlier detection methods and algorithms.
Strong programming skills in Python with expertise in Pandas, NumPy, Scikit-learn, or other data science packages.
Demonstrated ability to implement, improve, debug, and maintain machine learning models.
Experience in quantitative data analysis, including in-depth evaluations of complex issues.
Creative approach to engineering innovative features and signals into analytical solutions, pushing the boundaries of current tools and methodologies.
Applied knowledge of statistical data analysis for trend and anomaly identification, predictive modeling, and hypothesis testing.
Outstanding written and verbal communication skills, with the ability to make complex data science concepts understandable to non-technical audiences.
Highly proficient in Tableau.
Demonstrated experience in leading data science projects through all phases, including exploratory data analysis, data quality management, modeling, tool deployment, and presentation of results.
Experience with Docker, Kubernetes, Airflow, generative AI, and graph database is a plus.

Description:
In this role, you will be responsible for crafting and implementing machine learning models, establishing automated algorithms, and building tools to identify anomalies in metrics related to hardware field performance and product quality. Collaborate with multi-functional teams, establish relationships, identify opportunities, develop performance indicators, and communicate complex data analyses effectively.

Key Responsibilities:

Build and refine anomaly detection tools and algorithms, improving our ability to identify and respond earlier to emerging signals in our data.
Research and integrate new technologies and methods in data science, data engineering, and data visualization to elevate the technical proficiency and innovation of our team.
Construct and maintain dashboards and self-service tools, facilitating effective data analysis and sharing of insights.
Handle large, complex data sets and use advanced analytics to tackle challenging problems and enhance business processes.

Education & Experience: