ADSIPL
Technology
DataScientistII
Neural analysis suggests this role is
optimal for Mid candidates.
“Data Scientist II at ADSIPL. Skills: Data Science, Machine Learning, Statistical Modeling, Optimization. Demonstrate technical knowledge on feature engineering. Perform effective exploratory data analysis”
Industry & Context.
Problem solving; Quantitative modeling skills; Complex decision-making problems; Aptitude for solving unstructured problems
What They're Looking For.
Must Have
5+ years of data scientist experience, 3+ years of data querying languages experience, 3+ years of scripting languages experience, 3+ years of statistical/mathematical software experience, 3+ years of machine learning/statistical modeling experience, 3+ years of data analysis tools and techniques experience, Experience leading architecture and design of systems, Experience developing and deploying LLMs in production
Nice to Have
Ph.D. in Science, Technology, Engineering, or Mathematics, Experience in a ML or data scientist role with a large technology company
What You'll Do.
Demonstrate technical knowledge on feature engineering
Perform effective exploratory data analysis
Build models using Time Series Forecasting techniques
Formulate ensemble models
Design AI agents for supply chain optimization
Build evaluation frameworks with guardrails and benchmarks
Ensure reliability of AI agents
Own full development lifecycle adhering to responsible AI
Drive AI roadmap leveraging emerging agentic AI trends
Apply Supervised algorithms
Apply UnSupervised algorithms
Solve optimization problems
Perform hands on experience in Linear Programming
Work closely with internal stakeholders
Align stakeholders with respect to focus area
Solve unstructured problems
Work in a self-directed environment
Own tasks and drive them to completion
Develop and define key business questions
Build data sets that answer business questions
Work with distributed machine learning algorithms
Work with statistical algorithms
Harness enormous volumes of data at scale
How You'll Work.
Team & Collaboration
Global Stakeholders; Data Engineers; Business Intelligence Engineers; Business Analysts; Business teams; Engineering teams; Partner teams
Communication Scope
Translate data-driven findings; Business communication skills
Full Job Description
AWS Infrastructure Services owns the design, planning, delivery, and operation of all AWS global infrastructure. In other words, we’re the people who keep the cloud running. We support all AWS data centers and all of the servers, storage, networking, power, and cooling equipment that ensure our customers have continual access to the innovation they rely on. We work on the most challenging problems, with thousands of variables impacting the supply chain — and we’re looking for talented people who want to help. You’ll join a diverse team of software, hardware, and network engineers, supply chain specialists, security experts, operations managers, and other vital roles. You’ll collaborate with people across AWS to help us deliver the highest standards for safety and security while providing seemingly infinite capacity at the lowest possible cost for our customers. And you’ll experience an inclusive culture that welcomes bold ideas and empowers you to own them to completion. Do you love problem solving? Are you looking for real world Supply Chain challenges? Do you have a desire to make a major contribution to the future, in the rapid growth environment of Cloud Computing? Amazon Web Services is looking for a highly motivated, Data Scientist to help build scalable, predictive and prescriptive business analytics solutions that supports AWS Supply Chain and Procurement organization. You will be part of the Supply Chain Analytics team working with Global Stakeholders, Data Engineers, Business Intelligence Engineers and Business Analysts to achieve our goals. We are seeking an innovative and technically strong data scientist with a background in optimization, machine learning, and statistical modeling/analysis. This role requires a team member to have strong quantitative modeling skills and the ability to apply optimization/statistical/machine learning methods to complex decision-making problems, with data coming from various data sources. The candidate should have strong c
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