Position Summary...
What you'll do...
About The Team:
We invite you to become part of a team that is dedicated to the development of data-driven models and services. Our primary focus is on driving high-quality demand from a plethora of digital platforms, including Google, Bing, Facebook, and Pinterest, to Walmart's E-Commerce sites.
What you will do:
• The development and deployment of a foundational AI layer along with Gen AI models for online inferencing. This is crucial for supporting applications such as Ads moderation, social commerce, etc.
• The development and maintenance of a robust, scalable machine learning platform, facilitating end-to-end machine learning operations from model development and versioning to deployment.
• The creation, deployment, and maintenance of high-quality web-based dashboard systems to draw visual insights from our ever-expanding dataset, monitor machine learning models, and evaluate model performance via A/B testing.
• The development, testing, and deployment of big-data and machine learning pipelines, encompassing data ingestion, model production, and visualization.
• Efficient management of public cloud services, using public cloud tools and resources to scale our storage and computation capabilities on the Google Cloud Platform.
What you will bring:
We are looking for individuals who possess:
• Proficiency in at least one programming language such as Python, Java, C++, or JavaScript.
• A solid understanding of data structures and algorithms.
• Extensive knowledge and experience in distributed inferencing in GPUs, LLM serving framework, and PyTorch/Tensorflow, etc.
• Knowledge and experience in Linux, public cloud computing, Docker, and Kubernetes.
• Experience in the development of REST API services.
Data Strategy: Requires knowledge of understanding of business value and relevance of data and data enabled insights / decisions; Appropriate application and understanding of data ecosystem including Data Management, Data Quality Standards and Data Governance, Accessibility, Storage and Scalability, etc.; Understanding of the methods and applications that unlock the monetary value of data assets. To understand, articulate, and apply principles of the defined strategy to routine business problems that involve a single function.
Analytical Modeling: Requires knowledge of feature relevance and selection; Exploratory data analysis methods and techniques; Advanced statistical methods and best-practice advanced modelling techniques (e.g., graphical models, Bayesian inference, basic level of NLP, Vision, neural networks, SVM, Random Forest etc.); Multivariate calculus; Statistical models behind standard ML models; Advanced excel techniques and Programming languages like R/Python; Basic classical optimization techniques (e.g., Newton-Rapson methods, Gradient descent); Numerical methods of optimization (e.g. Linear Programming, Integer Programming, Quadratic Programming, etc.) To select the analytical modeling technique most suitable for the structured, complex data and develops custom analytical models. Conduct exploratory data analysis activities (for example, basic statistical analysis, hypothesis testing, statistical inferences) on available data. Define and finalize features based on model responses and introduces new or revised features to enhance the analysis and outcomes. Identify the dimensions of the experiment, finalize the design, test hypotheses, and conduct the experiment. Perform trend and cluster analysis on data to answer practical business problems and provide recommendations and key insights to the business. Mentor and guide junior associates on basic modeling and analytics techniques to solve complex problems.
Model Deployment and Scaling: Requires knowledge of impact of variables and features on model performance; understanding of servers, model formats to store models. To support efforts to ensure that analytical models and techniques used can be deployed into production. Support evaluation of the analytical model. Support the scalability and sustainability of analytical models.
Code Development and Testing: Requires knowledge of coding languages like SQL, Java, C++, Python and others; Testing methods such as static, dynamic, software composition analysis, manual penetration testing and others; Business, domain understanding. To write code to develop the required solution and application features by using the recommended programming language and leveraging business, technical, and data requirements. Test the code using the recommended testing approach.
Tech. Problem Formulation: Requires knowledge of Analytics/big data analytics / automation techniques and methods; Business understanding; Precedence and use cases; Business requirements and insights. To translate/ co-own business problems within one's discipline to data related or mathematical solutions. Identify appropriate methods/tools to be leveraged to provide a solution for the problem. Share use
Location: San Mateo, CA
Posted: Oct. 18, 2024, 8:24 p.m.
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