Sr. Data Science, Amazon Customer Service Data Analytics Support Hub
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 contentious scientific and architectural decisions.
- Partner with Data Engineers on productionization, Shepherd risk, App Security red-certification, Kale, Legal, Threat Models, for scientific assets.
- Mentor team members; provide promotion assessments; contribute to hiring at DS II and DS III. Represent Q&E in the broader Amazon Data Science community.
- Produce design docs, technical documentation, and review artifacts.
- Experience with data scripting languages (e.g., SQL, Python, R, or equivalent) or statistical/mathematical software (e.g., R, SAS, Matlab, or equivalent)
- Experience working as a Data Scientist
- Experience with statistical models e.g. multinomial logistic regression
- Experience with data scripting languages (e.g. SQL, Python, R etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab)
- PhD in computer science, machine learning, engineering, or related fields, or experience working as a Data Scientist
- Experience with machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance
- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Experience working with data engineers/business intelligence engineers collaboratively
- Experience leading applied LLM/GenAI programs end-to-end: prompt design, eval frameworks, RAG/agentic pipelines, safety and hallucination mitigation, cost/latency/scale trade-offs- Experience with data visualization using Tableau, Quicksight, or similar tools
- Experience building data pipelines or automated ETL processes
- Experience providing technical leadership and mentoring other engineers for best practices on data engineering
- Master's degree in econometrics, statistics, industrial engineering, operations research, optimization, data mining, analytics, or equivalent quantitative field, or Master's degree
- Experience in contact-center, conversational AI, or CX domains with multi-touchpoint journey analytics
- Experience navigating Amazon production processes: Shepherd, App Security, ASR, Kale, Legal, Threat Models,
Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
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 contentious scientific and architectural decisions.
- Partner with Data Engineers on productionization, Shepherd risk, App Security red-certification, Kale, Legal, Threat Models, for scientific assets.
- Mentor team members; provide promotion assessments; contribute to hiring at DS II and DS III. Represent Q&E in the broader Amazon Data Science community.
- Produce design docs, technical documentation, and review artifacts.
- Experience with data scripting languages (e.g., SQL, Python, R, or equivalent) or statistical/mathematical software (e.g., R, SAS, Matlab, or equivalent)
- Experience working as a Data Scientist
- Experience with statistical models e.g. multinomial logistic regression
- Experience with data scripting languages (e.g. SQL, Python, R etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab)
- PhD in computer science, machine learning, engineering, or related fields, or experience working as a Data Scientist
- Experience with machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance
- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Experience working with data engineers/business intelligence engineers collaboratively
- Experience leading applied LLM/GenAI programs end-to-end: prompt design, eval frameworks, RAG/agentic pipelines, safety and hallucination mitigation, cost/latency/scale trade-offs- Experience with data visualization using Tableau, Quicksight, or similar tools
- Experience building data pipelines or automated ETL processes
- Experience providing technical leadership and mentoring other engineers for best practices on data engineering
- Master's degree in econometrics, statistics, industrial engineering, operations research, optimization, data mining, analytics, or equivalent quantitative field, or Master's degree
- Experience in contact-center, conversational AI, or CX domains with multi-touchpoint journey analytics
- Experience navigating Amazon production processes: Shepherd, App Security, ASR, Kale, Legal, Threat Models,
Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.