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Amazon's Consumer Payments organization is seeking a highly quantitative, experienced Data Scientist to drive the development of science analytics and insights capabilities. You will succeed in this role if you are an organized self-starter who can learn new technologies quickly and excel in a fast-paced environment. In this position, you will be a key contributor and sparring partner, developing analytics and insights that global executive management teams and business leaders will use to define global strategies and deep dive businesses.
Our team is a brand-new Analytics team, offering a unique opportunity to build a new set of analytical experiences from the ground up. You will be part the team that is focused developing analytical solutions for our customers (Product/Marketing/Finance/Operations team). The position is based in India but will interact with global leaders and teams in Europe, Japan, US, and other regions. You should be highly analytical, resourceful, customer focused, team oriented, and have an ability to work independently under time constraints to meet deadlines. You will be comfortable thinking big and diving deep. A proven track record in taking on end-to-end ownership and successfully delivering results in a fast-paced, dynamic business environment is strongly preferred.
Key job responsibilities
. Working with technical and non-technical stakeholders across every step of science project life cycle.
. Design, develop, implement, test forecasting solutions for planning and goal setting exercises across various payment products and programs across CP organization.
. Apply statistical and machine learning techniques to extract meaningful trends and insights.
. Identifying real-time anomalies and early-detection mechanisms.
. Collaborate with Analysts, Business Intelligence Engineers and Product Managers to implement algorithms that exploit rich data sets for statistical analysis, and machine learning.
. Work with product tech teams, BIE/DE and build robust and scalable science solutions integration with in house reporting/BI tools using SQL, Python and Spark.
. Leading training and informational sessions on our science and capabilities.
. Write, share and present documents summarizing your findings and recommendations to all levels of organization.
. Well versed in the relevant literature and have the ability to identify, analyze, and adapt new state-of-the-art methods as well as beyond to create new problem-solution pairs to build breakthroughs.
. Communicate effectively with product/business/tech-teams/other science teams.
- 1+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 2+ years of data/research scientist, statistician or quantitative analyst in an internet-based company with complex and big data sources experience
- 1+ years of creating or contributing to mathematical textbooks, research papers, or educational content experience
- Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (STEM)
- Bachelor's or Master's degree in computer science, engineering or a related technical field
- Experience with data scripting languages (e.g. SQL, Python, R etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab)
- Experience using complex modeling and analysis to inform key business decisions
- Experience that includes strong analytical skills, attention to detail, and effective communication abilities, or experience in SQL Server/MySQL
- Experience with big data technologies such as AWS, Hadoop, Spark, Pig, Hive etc.
- Can work proactively and independently, meet deadlines, and deliver on projects and tasks
- Experience troubleshooting and documenting findings, or experience driving collaborative projects from conception to delivery
- Ph.D. in Science, Technology, Engineering, or Mathematics (STEM)
- Knowledge of statistical packages and business intelligence tools such as SPSS, SAS, S-PLUS, or R
- Knowledge of machine learning concepts and their application to reasoning and problem-solving
- Experience with clustered data processing (e.g., Hadoop, Spark, Map-reduce, and Hive)
- Experience working with or evaluating AI systems
- Experience applying quantitative analysis to solve business problems and making data-driven business decisions
- Experience effectively communicating complex concepts through written and verbal communication
- Master's degree or above in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science, or PhD
- Knowledge of AWS platforms such as S3, Glue, Athena, Sagemaker
- Knowledge of software engineering best practices across the development life cycle, including agile methodologies, coding standards, code reviews, source management, build processes, testing, and operations
- Experience in complex problem solving, and working in a tight schedule environment
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 for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.
Job ID: 147419057
Skills:
statistical concepts , Numpy, Machine Learning, Data Modeling, Pandas, Data Extraction, Excel, Python, Sql, Data Analysis
Skills:
Tensorflow, Nlp, Gcp, Pytorch, Docker, FastAPI, Azure, AWS, ASR, LLMs, TTS, Speech Audio ML systems, real-time systems, streaming pipelines, OpenAI Gemini
Skills:
Sql, Nosql, Tensorflow, Git, Pytorch, Gcp, Keras, Azure, Python, AWS, Airflow, scikit-learn, MLflow
Skills:
causal inference , Pandas, XGBoost, Python, Sql, scikit-learn, incrementality studies
Skills:
PostgreSQL, Rdf, Neo4j, Python, AWS, Apis, Gcp, MLops, Azure, pgvector, LLM-driven applications, vector databases, Pinecone, GenAIOps, LangGraph, Chroma, GenAI microservices, RAG pipelines, architecture standards, LangChain, knowledge-grounding frameworks, cloud platforms, benchmark model performance, prompt engineering, knowledge graph systems, Transformers, Milvus, Weaviate, LlamaIndex
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