ADCI HYD 13 SEZ
Data Science, Science, transportation and logistics
DataScientist,WWDSPAnalytics
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Data Scientist, WW DSP Analytics at ADCI HYD 13 SEZ. Skills: Data Science, Machine Learning, Analytics. Design data science solutions. Implement data science solutions”
Industry & Context.
Logical thinking; Quantitative analysis; Problem-solving
What They're Looking For.
Must Have
1+ years data querying languages, 1+ years scripting languages, 1+ years statistical/mathematical software, 2+ years data/research scientist, 2+ years statistician, 2+ years quantitative analyst, 1+ years creating mathematical textbooks, 1+ years creating research papers, 1+ years creating educational content, Master's degree in STEM, Experience in STEM
Nice to Have
Ph.D. in STEM, Knowledge of statistical packages, Knowledge of business intelligence tools, Knowledge of machine learning concepts, Experience with clustered data processing, Experience working with AI systems, Experience evaluating AI systems, Experience applying quantitative analysis, Experience making data-driven decisions
What You'll Do.
Design data science solutions
Implement data science solutions
Optimize DSP operations
Optimize capacity planning
Optimize performance measurement
Apply machine learning techniques
Improve DSP analytics
Improve forecasting models
Improve performance measurement systems
Analyze sentiment risks
Support DSP program management
Analyze business requirements
Calculate return on investment
Translate requirements into models
Apply models to predict ROI
Build scalable data models
Build automated reporting systems
Improve developer productivity
Raise data quality bar
Mine complex datasets
Analyze complex datasets
Generate actionable insights
Measure analytical risks
Align research focuses
Validate analytical approaches
Ensure insights drive outcomes
How You'll Work.
Team & Collaboration
Internal stakeholders; DSP owners; Cross-functional teams; Data Engineers; Business Intelligence Engineers; Program teams; DSP stakeholders
Full Job Description
The WW DSP Analytics team is a centralized analytics organization within Amazon's Last Mile Delivery Service Partner (DSP) program. We build best-in-class solutions that enable data-driven decision making across our global DSP ecosystem. Our team partners with internal stakeholders, DSP owners, and cross-functional teams to deliver insights that drive operational excellence, business growth, and the success of small business owners in Last Mile delivery. Our work directly impacts customer experience, driver and station associate experience, DSP success, and Amazon's sustainable growth. We are seeking a passionate Data Scientist with strong machine learning and analytical skills to join our team. You will work on challenging problems in the delivery planning space, applying data science rigor to generate actionable insights that support DSP performance measurement and continuous improvement. Key job responsibilities Develop Science Solutions for DSP Performance: Design and implement data science solutions to optimize Delivery Service Partner (DSP) operations, capacity planning, and performance measurement across the global DSP network Apply Advanced Machine Learning Techniques: Leverage solid research experience in Machine Learning and statistical modeling to identify opportunities for improving DSP analytics, forecasting models, and performance measurement systems Optimize DSP Program Policies and Sentiment Risks: Analyze sentiment risks and enhance existing algorithms that support DSP program management, including scorecard metrics, capacity reliability models, and performance evaluation frameworks Analyze Business Requirements with Return on Investment (ROI) calculation: Demonstrate superior logical thinking by quickly approaching large, ambiguous problems, translating high-level DSP program requirements into mathematical models, and applying models to predict the return on investment. Build Production-Scale Analytics: Contribute to the development and deployment
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