ADCI
Business Intelligence, Business Intel Engineer, ecp
Manager,ResearchAnalysis
Neural analysis suggests this role is
optimal for Manager candidates.
“Manager, Research Analysis at ADCI. Skills: ML research, ML solutions, Generative AI, AI/ML systems. Lead a team of Research Analysts. Define the AI strategy for AI/ML and GenAI”
Industry & Context.
Solving complex business challenges
What They're Looking For.
Must Have
Experience with SQL, Experience with R, Python, Weka, SAS, Matlab or other statistical/machine learning software, Bachelor's degree in a quantitative field, or experience working in Science, Technology, Engineering, or Mathematics (STEM), Experience demonstrating analytical abilities and confidence in the use of data, Experience communicating results to senior leadership, or experience in solving complex business challenges by delivering accurate and timely financial models, analysis, and recommendations that have a proven impact on business (e. g. , financial savings, operational improvements, or customer benefits), 5+ years of working with Data & AI related technologies, Experience working cross functionally with tech and non-tech teams
Nice to Have
Master's degree, or Master's degree and 3+ years of practical machine learning experience, Diverse experience will be favored eg. a mix of experience across different roles, In-depth understanding of machine learning concepts including developing models and tuning the hyper-parameters, as well as deploying models and building ML service, Technical expertise, experience in AI/ML and GenAI
What You'll Do.
Lead a team of Research Analysts
Define the AI strategy for AI/ML and GenAI
Lead complex initiatives
Establish technical standards
Influence executive-level decisions
Architect and own large-scale AI/ML systems
Deploy distributed LLM
Fine-tune infrastructure
Develop causal modeling
Partner with business
science and engineering teams
Identify and solve complex problems
Conduct ML experimentation
Make ML model design choices
Make architecture design choices
Develop prompt abstraction layers
Develop auto-evaluation techniques
Develop auto-prompt generation techniques
Lead initiatives on LLM Agents
Lead initiatives on RAG
Lead initiatives on inference optimization
Lead initiatives on low latency serving
Evaluate model safety
Evaluate model fairness
Drive responsible AI development
Align AI capabilities with product vision
Align AI capabilities with customer needs
Help the team leverage expertise
How You'll Work.
Team & Collaboration
Partner with business teams; Partner with science teams; Partner with engineering teams; Cross-functional teams
Communication Scope
Communicate results to senior leadership
Full Job Description
RBS (Retail Business Services) Tech team works towards enhancing the customer experience (CX) and their trust in product data by providing technologies to find and fix Amazon CX defects at scale. Our platforms help in improving the CX in all phases of customer journey, including selection, discoverability & fulfilment, buying experience and post-buying experience (product quality and customer returns). As a Sciences team in RBS Tech, we focus on foundational ML research and develop scalable state-of-the-art ML solutions to solve the problems covering customer experience (CX) and Selling partner experience (SPX). We work to solve problems related to multi-modal entity extraction and slot tagging for Catalog completeness and consistency, supervised and unsupervised techniques, multi-task learning, multi-label classification, aspect and topic extraction for Customer Anecdote Mining, image and text similarity and retrieval using NLP and Computer Vision for product groupings and identifying duplicate listings in product search results. Key job responsibilities As a Manager, Research Analysis, you will lead a team of Research Analysts (RA), and define the AI strategy for AI/ML and GenAI systems across the organization. You will lead complex initiatives, establish technical standards, and influence executive-level decisions through innovation and deep research. * Architect and own large-scale AI/ML systems, including distributed LLM deployments, fine-tuning infrastructure, and causal modeling * You will partner with business, science and engineering teams to identify and solve large and significantly complex problems that require scientific innovation. * Conduct thorough ML experimentation and make ML model and architecture design choices in the areas of developing prompt abstraction layers, auto-evaluation and auto-prompt generation techniques. * Lead initiatives on LLM Agents, RAG, inference optimization (quantization, pruning), and low latency serving * Evaluate model s
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