Machine Learning Engineer, Data Science / Analytics Lead and Manufacturing Decision Support
Location: Cupertino, California
Type: Full Time
Preferred Education: Masters
Internal Number: 200298101
Location: Cupertino, CA
Imagine what you could do here. At Apple, new ideas have a way of becoming phenomenal products, services, and customer experiences very quickly. Every single day, people do amazing things at Apple. Do you want to impact the future at Apple by developing an extraordinary platform for our Operations team?
Apple is a data-centric company where many critical decisions are made based on data. The completeness, accuracy and timeliness of data has huge implication to our decision-making. Every shipped Apple product undergoes rigorous testing at our factories to ensure the best customer experience. Our team handles the collection and reporting of all manufacturing data.
Machine Learning Algorithm Engineer
Through the use of statistics, the scientific process, and machine learning, the team recommends and implements solutions to the most challenging problems. We’re looking for experienced machine learning professionals to help us revolutionize how we manufacture Apple’s amazing products. Put your experience to work in this highly visible role.
Collaborate with fellow ML engineers, robotics/automation specialists, manufacturing and product development engineers to apply Machine Learning solutions to industrial problems and situations
Work with other ML engineer and program manager, analyze huge amounts of real-world production data to formulate the problem, propose modeling solutions. Your solution can be traditional ML (regression, classification) and/or Deep Learning .
Prototype and package your solutions in Python and/or C/C++/Objective-C towards roll-out of a data automation system
Participate Apple’s ML community activities and share your findings/innovations
Research and improve existing ML algorithms for various applications (images, and/or non-image ML algorithms )
You are encouraged to apply if you meet one of the below three requirements
Good knowledge and hands-on experience in image ML and computer vision
Good knowledge and hands-on experience in traditional ML (regression, classification, tree-ensembles, etc) for non-image data
Operation Research experts with some understanding of ML.
Strong software development skills with proficiency in Python, familiar with Git. Experienced user of machine learning and statistical-analysis libraries, such as scikit-learn, scipy, NetworkX, Spacy, and NLTK
Hands-on experience with design, implementation and application of ML/AI/Deep Learning solutions and techniques to build models that solve real problems.
Strong in communication and presentation skill.
[Plus] Experience of applying ML to industrial or manufacturing environments is a plus.
[Plus] Experience applying deep learning frameworks, such as PyTorch/Torch, Caffe2, TensorFlow, Keras, Theano to real world applications that solve problems
Data Science Analytics Lead
You will utilize data, infrastructure and intelligence tools to tackle interesting problems every day. You will be tasked with finding insights from data that will improve Product Operations, quality, and manufacturing efficiencies by understanding the variables impacting yield. The Product Operations Data team drives strategic initiatives for better data collection and reporting, ensure data integrity across multiple data sources, and reduce analysis time through automation and creative solutions.
You should have expertise required to understand complex data sets: product testing, parametric data, manufacturing, robotics and capital equipment. Then select and configure appropriate technologies and programming languages required to ensure successful business impact.
As a Sr. Data Analytics Lead you will drive and lead business insights, presenting data findings to peers, managers, directors, and VPs.
Highlight data patterns that could be useful for making business decisions.
Employ statistical techniques with big data initiatives and tools to drive major operational business decisions.
Answer complex questions through data science, analysis, and clearly communicate findings to multi-functional teams for direction.
Influence repair processes and fraud detection improvements by scripting analysis on very high volumes of data at a commodity and parametric level.
Seek opportunities to improve data collection, reporting and consumption based on business needs.
Regularly collaborate with internal and external information technology teams on resolving data issues, as well as mitigation plans to avoid errors in the future.
Participate in strategic capital systems planning.
Excellent analytical skills, advanced level of statistics with the ability to identify and predict trends and anomalies.
Strong expertise with Python, R and libraries such as (scikit- learn, scipy, R, NetworkX, Spacy, and NLTK).
Experience in data mining very large data sets, high proficiency in SQL (Teradata, SQL, Oracle, or MySQL or other RDBMS.)
Experience pulling your own data for analysis with Hive queries or in a distributed processing systems environment preferred (Hadoop, HDFS, Spark, AWS Redshift, Presto)
Data visualization experience with tools such as: Tableau, JMP, R creating dashboards and presenting data through reports.
Manufacturing Decision Support
The iPhone Capacity Planning team is a high impact and well valued team. From capital equipment planning, business allocation, production line planning to labor planning, the outcome of this team’s work influences key inputs for multi-billion dollar budgets and navigates complex operational challenges by working closely with supply demand planning, operation/technical program management, CapEx management, quality, infrastructure, AppleCare, procurement and our contract manufacturing partners.
On a day to day basis the team plays a meaningful role in defining and leading strategic and operational priorities as well as improvements across the iPhone and Core Technologies organization. The ideal candidate will help us to go further into the areas of deep data analytics and operations research in order to drive decision support.
Experience planning supply chain and operational structures in complex and dynamic environment.
Hands-on experience in complex data modeling, simulation and optimization.
Experience in developing solutions for open ended projects with multi-cross functional teams, preferably in the context of factory operations
Experience with data acquisition tools, large datasets and data mining
Experience in relevant coding languages: C/C++, R, or Python
Solid understanding of optimization tools such as: CPLEX, AMPL, Gurobi or FICO Xpress.
Working knowledge of predictive modeling
Self-sufficient with an ability to excel in an environment of autonomy amidst ambiguity. Identify areas within operations that would benefit from your analysis and feedback.
Strong communication and analytical skills - adept at messaging domain and technical content, at a level appropriate for the audience.
Data analytics — taking tons of data and helping build out or select tools to analyze production, capacity, lead times, cost structures, etc.
Data modeling, simulations or optimization tools like Gurobi, Cplex or Xpress
Programming experience with Java, C++, Python or Perl
Excellent communication skills, both verbal and written
We’re a diverse collective of thinkers and doers, continually reimagining what’s possible. And every new product, service, or feature we invent is the result of people working together to make each other’s ideas stronger.