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Cupertino, California, 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.   Responsibilities:   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 )   Requirements:   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. Responsibilities: 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. Requirements:   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. Responsibilities: 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. Requirements 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

Nationwide, POSITION SUMMARY  The Research Associate (Simulation) applies the science of distribution to Fortna’s products and services by building simulation and mathematical models to support distribution center design, by developing novel algorithms for Fortna’s Warehouse Execution System (FortnaWES™), and by processing data to support solution design projects. The overarching goal is to build Fortna’s intellectual property (IP) as embodied in the FortnaDCdesign Suite™ and FortnaWES™. PRIMARY RESPONSIBILITIES: Develop simulation and mathematical models of distribution operations in order to answer design questions for projects and to give insight to other Fortna Associates. Develop models and algorithms for FortnaWES™ in consultation with the leadership of Solutions R&D and Product (Software) R&D. Serve as a Data Team member by running FortnaDCmodeler® on Operations Design projects and supporting teams with needed data analysis and modeling. Collaborate with other Associates to create thought leadership around Fortna IP to further Fortna’s brand in the marketplace. Assist with efforts to train Associates on the application of Fortna IP. REQUIRED QUALIFICATIONS: Research-based master’s degree in Industrial and Systems Engineering, Systems Engineering, or a related technical field Experience developing discrete-event simulation models of manufacturing or distribution operations using a commercial discrete-event simulation package. Experience with FlexSim is preferred, though not required. Experience using analytical models to represent production or logistics systems Strong interpersonal, organizational and time management skills and the desire to work in a team environment Excellent written and oral communication skills Excellent collaboration skills Excellent structured problem-solving skills Excellent project management skills Ability to travel domestically Ability to perform essential functions of the job   DESIRED QUALIFICATIONS: PhD in Industrial and Systems Engineering or Systems Engineering Experience developing mathematical models (optimization, stochastic, computational) for distribution center design and operations Excellent technical communication skills as evidenced by journal articles, white papers, conference papers/presentations, corporate presentations, etc. Experience with data analysis through SQL-based packages Experience with software tool development, especially Python. Experience educating and training others   WORKING CONDITIONS: When duties are performed in a typical office environment, extended periods of sitting at a desk and viewing a computer screen will be required. Also required is the ability to talk and hear, in person and by telephone; use of hands to handle, feel, or operate standard office equipment; and ability to reach with hands and arms. Associates are frequently required to walk and stand. The noise level in this work environment is usually quiet. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions of this position. When travelling to Client sites, essential requirements of this position may require physical presence in various environments and locations. Physical stamina may be required for prolonged standing, bending, stooping, and/or working in cramped quarters. Exposure to potentially dangerous tools and equipment amidst a variety of building materials is probable, as is occasional exposure to moving mechanical parts. The noise level in the work environment can vary from being relatively quiet, to moderate, to excessive. Safety shoes or boots may be required in certain situations. Additional safety clothing including gloves, hardhats, and devices to protect eyes, mouth, or hearing, will be worn as necessary. This position description should not be considered all-inclusive.

Lake Buena Vista, Florida, Job Description We seek candidates for roles at multiple levels within the Decision Science team. The specific job level for an individual candidate will be determined based on their education, prior experience, and demonstrated technical and leadership proficiencies. The Decision Science team focuses on creating innovative mathematical models to inform business decision making through data. The team is interested in all fields related to data science, with emphasis on statistics, operations research, econometrics, and machine learning. The Decision Science team resides within the Disney Decision Science + Integration (DDSI) organization. DDSI provides internal consulting services for clients across The Walt Disney Company, including Disney Parks, Experiences and Products (covering Parks & Resorts, Signature Experiences, and Consumer Products, Games and Publishing) and Disney Media & Entertainment Distribution (covering content from Studios, General Entertainment and Sports). The DDSI team sits at the intersection of business strategy, advanced analytics, and technology integration to help our partners explore opportunities for analytics, shape business decisions, and drive value. Our work includes conceptualizing new solutions, solution design and development, implementation and integration with business processes, and ongoing business support.   Responsibilities Achieve business goals and objectives—Research and develop decision science models and act as a consultant to all business units within The Walt Disney Company Pursue innovation—Conduct research on analytical techniques and translate that research into usable and sustainable solutions for users Put your skills to the test—Perform data collection and data mining, and build state-of-the-art decision science algorithms, tools, and systems Drive value—Model and analyze revenue management and pricing related issues using various mathematical, statistical, and simulation techniques Tell the story—Present science results to business partners and clients Support a global and enterprise-wide mission—Identify and apply best practices in the field of advanced analytics for multiple businesses such as theme parks and resorts (e.g. Walt Disney World), Disney Cruise Line, media (e.g. ABC, ESPN), movies (e.g. Walt Disney Animation Studios), live shows (e.g. Disney on Broadway), and retail (e.g. shopDisney) Basic Qualifications Must exhibit competence in at least one of the following four analytical fields, inclusive of topics as noted: Statistics (two or more topics): Bayesian statistics Generalized linear models Mixture models Nonparametric regression Structural equation models Time series (state space models, ARIMA, etc.) Optimization (at least one topic): Decision analysis or multiple criteria decision making Mixed-integer optimization Nonlinear optimization Stochastic optimization Discrete-event simulation and stochastic models Econometrics (two or more topics): Generalized method of moments Instrumental variables Limited dependent variables Panel data Simultaneous equations models Machine learning (two or more topics): Boosting CART Clustering Graphical models Neural networks Random forests Reinforcement learning Support vector machines Must exhibit competence in at least one computing topic: General computing (Python, Java, C, C++, etc.) Statistical computing (R, SAS, etc.) Scientific computing (Julia, MATLAB, etc.) Roles beyond the entry level require the ability to work with little guidance on science methodologies and modeling approaches, based on sound business acumen, and to provide peer leadership to junior colleagues Higher roles require the ability to work concurrently across lines of business or functional areas, to direct the design and development of advanced analytical models, or to provide project leadership to ensure successful implementation of solutions across all project phases Preferred Qualifications Proven knowledge of revenue management context, including demand forecasting, resource allocation, and pricing Required Education Master’s or Ph.D. degree in Statistics, Operations Research, Industrial Engineering, Mathematics, Machine Learning, Econometrics, or related field

INFORMS Journal on Computing, Ahead of Print.

INFORMS Journal on Computing, Ahead of Print.

INFORMS Journal on Computing, Ahead of Print.

INFORMS Journal on Computing, Ahead of Print.

INFORMS Journal on Computing, Ahead of Print.