Amazon EU Sarl
Technology
Sr.DataScience,AmazonCustomerServiceDataAnalyticsSupportHub
Neural analysis suggests this role is
optimal for Senior candidates.
“Sr. Data Science, Amazon Customer Service Data Analytics Support Hub at Amazon EU Sarl. Skills: Data Science, Advanced Analytics, Machine Learning, LLM/GenAI. Set scientific direction. Define measurement frameworks”
Industry & Context.
Root cause analysis; Troubleshooting
What They're Looking For.
Must Have
Experience with data scripting languages (SQL, Python, R), Experience working as a Data Scientist, Experience with statistical models, Experience with machine learning/statistical modeling data analysis tools, Experience with AWS technologies, Experience working with data engineers, Experience working with business intelligence engineers, Experience leading applied LLM/GenAI programs end-to-end
Nice to Have
Master's degree in econometrics, statistics, industrial engineering, operations research, optimization, data mining, analytics, or equivalent quantitative field, Experience in contact-center, conversational AI, or CX domains, Experience with multi-touchpoint journey analytics, Experience navigating Amazon production processes
What You'll Do.
Set scientific direction
Define measurement frameworks
Own scientific framework for multi-contact journey analysis
Choose appropriate methods
Drive excellence in evaluation
Design driver-analysis methods
Design bridging methods
Represent DASH in reviews
Build consensus on decisions
Partner with Data Engineers on productionization
Ensure App Security red-certification
Work with Threat Models
Provide promotion contributions
Represent Q&E in community
Produce technical documentation
How You'll Work.
Team & Collaboration
Partner teams; Senior Manager reviews; Director reviews; CS-LT forums; Partner-team design reviews; Data Engineers; Business intelligence engineers; Amazon Data Science community
Communication Scope
Scientific voice; Scientific leader; Scientific strategy; Scientific bar; Scientific assets; Scientific direction; Scientific framework; Scientific and architectural decisions
Full Job Description
Amazon's Customer Service (CS) department is seeking a senior Data Scientist to lead the scientific direction of the Data Analytics Support Hub (DASH) Advanced Analytics team. CS is the heart of Amazon; our vision is to be Earth's most customer-centric company. The successful candidate will be the scientific leader within the Advanced Analytics branch, setting the methodological bar and driving Q&E's most complex diagnostic and predictive analytics across a worldwide, cross-vertical scope. As a Data Scientist III, you will define the scientific strategy for Q&E's transition from descriptive to diagnostic and predictive analytics. You will own the measurement frameworks for pioneering KPIs where no prior art exists, lead the multi-contact journey science (Transfers, Repeats, DART, ECR/VPI), and be the scientific voice in partnership with central teams. You will be hands-on on 2-3 flagship programs while being accountable for the scientific bar across the entire branch. Key job responsibilities Responsibilities include but are not limited to: - Set the scientific direction for the Advanced Analytics branch across flagship initiatives. - Define measurement frameworks for Q&E-pioneering KPIs where no prior art exists (QoS, FIR, Outlier Behavior). - Own the scientific framework for multi-contact journey analysis: threading interactions, attributing root cause across touchpoints, separating preventable vs. necessary events. - Choose the right methods (statistical, causal, ML, LLM, hybrid) for each problem and justify trade-offs. Drive excellence in evaluation: ground-truth construction with Quality auditors, human audits, precision/recall, drift, calibration, bias, safety, and cost. - Design driver-analysis and bridging methods that explain KPI movement (WoW, MoM, YoY, vs OP2) across dimensions for WBR "why" automation consumed by senior leadership. - Represent DASH in Senior Manager / Director reviews, CS-LT forums, and partner-team design reviews. Build consensus on con
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