Job Description

DESCRIPTION
As a Research Analyst, you'll collaborate with experts to develop ML models leveraging big data solutions and Large Language Models (LLMs) for business needs. You'll drive product pilots, demonstrating innovative thinking and customer focus. You'll build scalable solutions, write high-quality code, and develop state-of-the-art ML models. You'll coordinate between science and software teams, optimizing solutions. The role requires thriving in ambiguous, fast-paced environments and working independently with ML models.
Key job responsibilities

• Collaborate with seasoned Applied Scientists and propose best in class ML solutions for business requirements
• Dive deep to drive product pilots, demonstrate think big and customer obsession LPs to steer the product roadmap
• Build scalable solutions in partnership with Applied Scientists by developing technical intuition to write high quality code and develop state of the art ML models utilizing most recent research breakthroughs in academia and industry
• Coordinate design efforts between Sciences and Software teams to deliver optimized solutions
• Ability to thrive in an ambiguous, uncertain and fast moving ML usecase developments.
• Familiar with ML models and work independent.
• Mentor Junior Research Analyst (RAs) and contribute to RA hiring

About the team
Retail Business Services Technology (RBS Tech) team develops the systems and science to accelerate Amazon's flywheel. The team drives three core themes: 1) Find and Fix all customer and selling partner experience (CX and SPX) defects using technology, 2) Generate comprehensive insights for brand growth opportunities, and 3) Completely automate Stores tasks.
Our vision for MLOE is to achieve ML operational excellence across Amazon through continuous innovation, scalable infrastructure, and a data-driven approach to optimize value, efficiency, and reliability. We focus on key areas for enhancing machine learning operations: a) Model Evaluation: Expanding LLM-based audit platform to support multilingual and multimodal auditing. Developing an LLM-powered testing framework for conversational systems to automate the validation of conversational flows, ensuring scalable, accurate, and efficient end-to-end testing. b) Guardrails: Building common guardrail APIs that teams can integrate to detect and prevent egregious errors, knowledge grounding issues, PII breaches, and biases. c) Deployment Framework support LLM deployments and seamlessly integrate it with our release management processes.
BASIC QUALIFICATIONS
- o Bachelor's degree in Quantitative or STEM disciplines (Science, Technology, Engineering, Mathematics)
- o 3+ years of relevant work experience in solving real world business problems using machine learning, deep learning, data mining and statistical algorithms
- o Strong hands-on programming skills in Python, SQL, Hadoop/Hive. Additional knowledge of Spark, Scala, R, Java desired but not mandatory
- o Strong analytical thinking
- o Ability to creatively solve business problems, innovating new approaches where required and articulating ideas to a wide range of audiences using strong data, written and verbal communication skills
- o Ability to collaborate effectively across multiple teams and stakeholders, including development teams, product management and operations.
PREFERRED QUALIFICATIONS
- o Master's degree with specialization in ML, NLP or Computer Vision preferred
- o 3+ years relevant work experience in a related field/s (project management, customer advocate, product owner, engineering, business analysis)
- o 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 Data science, ML and Statistics
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