Job Description

Locations

• Cupertino, California, United States 95014

Salary

Skills Required

• predictive analyticsintermediate
• analytic engineeringintermediate
• machine learningintermediate
• developing scalable ML algorithmsintermediate
• designing end-to-end machine learning pipelinesintermediate
• MLOps frameworksintermediate
• feature engineeringintermediate
• model servingintermediate
• deep learningintermediate
• traditional ML solutionsintermediate
• high-volume datasetsintermediate
• GPU clustersintermediate
• distributed CPU environmentsintermediate
• cutting-edge algorithmsintermediate
• data qualityintermediate
• algorithm developmentintermediate
• feature developmentintermediate
• predictive modelingintermediate
• visualizationintermediate
• deploymentintermediate
• maintenanceintermediate
• advanced ML methodsintermediate
• prototyping analysis pipelinesintermediate
• collaboration with data engineersintermediate
• operationalizing modelsintermediate
• agile solution enhancementintermediate

Required Qualifications

• 8+ years of hands-on programming skills for large-scale data processing (experience, 8 years)
• Graduate degree required in Computer Science, Statistics, Data Mining, Machine Learning, Operations Research, or related field (degree in computer science)

Preferred Qualifications

• Excellent understanding of analytical methods and machine learning algorithms including regression, clustering, classification, optimization, and other advanced analytic techniques. (experience)
• 8+ years of proven experience building and scaling predictive models across distributed systems (eg: Spark, Kubernetes, GPU clusters), production model hosting, and handling end-to-end performance optimization to solve business problems. (experience, 8 years)
• 8+ years of hands-on programming skills (Python, and/or Spark) for large-scale data processing, deriving key insights, developing machine learning models on structured and unstructured data, and with demonstrated success maintaining robust, high-throughput ML pipelines in a production environment. (experience, 8 years)
• Comfortable with advanced deep learning frameworks (Tensorflow, PyTorch) and adept at designing and scaling ML platforms that include feature stores, automated retraining pipelines and CI/CD integration. Able to design systems to handle high-volume ML workflows and implement scalable, fault-tolerant solutions. (experience)
• Solid technical database and data modeling knowledge (Oracle, Hadoop, SnowFlake), and experience optimizing SQL queries on large dataset for performance-critical analytics. (experience)
• Able to work effectively on ambiguous data and constructs within a fast-changing environment, tight deadlines and priority changes (experience)
• Strong communication skills and ability to explain complex technical topics to both data science peers and non-technical business stakeholders, effectively presenting findings and recommendations to senior executives. (experience)
• Demonstrated success in partnering cross-functionally, guiding diverse technical teams, aligning business stakeholders, invested in collective success of teams and project outcomes. (experience)

Responsibilities

• Support Product Marketing, Investor Relations, and the Executive Team with predictive analytics for customer product and services engagement. Understand product requirements then translate them into modeling tasks and engineering tasks
• Develop scalable ML algorithms and models to understand customer behavior and provide leadership with actionable insights and recommendations
• Design and implement end-to-end machine learning pipelines—from feature engineering to model serving— using best in class MLOps frameworks
• Develop and optimize deep learning and traditional ML solutions on high-volume datasets using GPU clusters or distributed CPU environments.
• Experiment with cutting-edge algorithms, providing advanced insights into customer behavior and engagement.
• Manage ML projects through all phases, including data quality, algorithm/feature development, predictive modeling, visualization, and deployment and maintenance.
• Tackle difficult, non-routine analysis/prediction problems, applying advanced ML methods as needed.
• Partner with peers to build and prototype analysis pipelines that provide insights at scale.
• Collaborate with data engineers and infrastructure partners to implement robust solutions and operationalize models. Enhance and evolve solutions to meet changing business needs with agility.

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