Job Location: Bangalore
- Bachelors with 10+ years or Masters with 8+ years of experience in Data Science and ML
- Experience in Python, R or another scripting language, command line/notebook usage
- Proven experience in working with databases and SQL in a business environment
- Strong analytical and quantitative skills with the ability to use data and metrics to back up assumptions, recommendations and drive actions
- Knowledge of various machine learning techniques and key parameters that affect their performance
- Ability to work backwards from customer needs, and distill informal customer requirements into problem definitions, dealing with ambiguity and competing objectives
- Superior verbal and written communication skills, ability to convey rigorous mathematical concepts and considerations to non-expert audience
Job summary
How to use the world’s richest collection of e-commerce data to improve payments experience for our customers? Amazon Consumer Payments Global Data Science team seeks a Senior Data Scientist for building analytical and scientific solutions that will address increasingly complex business questions in the Gift-Cards space.
Amazon.com has a culture of data-driven decision-making and demands intelligence that is timely, accurate, and actionable. This team operates at WW level and provides a fast-paced environment where every day brings new challenges and opportunities.
As a Senior Data Scientist in this team, you will be driving the data science roadmap and will provide descriptive, predictive or prescriptive solutions to the gift-cards business team. You will develop statistical and machine learning models, design and run experiments, and find new ways to optimize the customer experience. You will need to collaborate effectively with internal stakeholders, cross-functional teams to solve problems, create operational efficiencies, and deliver successfully against high organizational standards. You will also work on cross-disciplinary efforts with other scientists within Amazon.
Responsibilities
- You should be detail-oriented and must have an aptitude for solving unstructured and ambiguous problems. You should work in a self-directed environment, own tasks and drive them to completion
- You should be passionate about working with huge data sets and be someone who loves to bring datasets together to answer business questions. You should have deep expertise in creation and management of datasets
- You should demonstrate thorough technical expertise on feature engineering of massive datasets, exploratory data analysis, and model building using supervised and unsupervised learning algorithms – Random Forest, Gradient Boosting, SVM, Neural Nets etc. You should be aware of automating feedback loops for algorithms in production
- Work closely with internal stakeholders like the business teams, engineering teams and partner teams and align them with respect to your focus area
- You should have excellent business and communication skills to be able to work with business owners to develop and define key business questions and build mechanisms that answer those questions
- Innovate by adapting new modeling techniques and algorithms
- You should have exposure at implementing and operating stable, scalable data flow solutions from production systems into end-user facing applications/reports. These solutions will be fault tolerant, self-healing and adaptive
- Your teams will work with distributed machine learning and statistical algorithms upon a large Hadoop cluster to harness enormous volumes of online data at scale to serve our customers
- Experience in building & implementing scalable machine learning models in production to support real-time inference is a plus
- Experience in creating powerful data driven visualizations to describe your ML modeling results to stakeholders
- Compelling communication and influencing skills and demonstrated experience in engaging and influencing senior executives
- Familiarity with AWS SageMaker is a plus
- Publications or presentations at Machine Learning, Deep Learning and Data Mining journals/conferences is a plus
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