Amazon

Data Scientist, AFFS

Posted On

Aug. 20, 2024

Location

Pune, Maharashtra

Experience Level

Freshers/Experienced

Job Type

Data Science

Apply By

Oct. 1, 2024

Job Description

Amazon Financials Foundation Services (AFFS), a division within Amazon’s eCommerce Services Organization (eCS) is leading innovation in business systems integration and defining the future of financial accounting at Amazon scale. Our systems are advancing one of the world's most scalable, reliable, and secure e-commerce ecosystem and responsible for processing hundreds of billions of dollars in transactions, in multiple currencies and countries. We are at the center of Amazon’s key initiatives and fueling the growth of Amazon’s businesses worldwide by constantly raising the bar on the speed at which business teams can integrate with our systems.

Do you love problem solving? Are you looking for real world accounting challenges at massive scale ? Do you have a desire to change the way accounting does accounting ?

Amazon Financial Foundation Services is looking for a highly motivated Data Scientist to help build scalable, predictive and prescriptive business analytics & Machine Learning solutions that supports AFFS organization. You will be working with Global Stakeholders, Data Engineers, Business Intelligence Engineers and Business Analysts to achieve our goals.

We are seeking an innovative and technically strong data scientist with a background in optimisation, machine learning, and statistical modeling/analysis. This role requires a team member to have strong quantitative modeling skills and the ability to apply optimisation/statistical/machine learning methods to complex decision-making problems, with data coming from various data sources. The candidate should have strong communication skills, be able to work closely with stakeholders and translate data-driven findings into actionable insights. The successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and ability to work in a fast-paced and ever-changing environment.

Key job responsibilities
- Demonstrate thorough technical knowledge on feature engineering of massive datasets, effective exploratory data analysis, and model building using industry standard time Series Forecasting techniques and formulate ensemble model.
- Proficiency in both Supervised(Linear/Logistic Regression) and UnSupervised algorithms(k means clustering, Principle Component Analysis, Market Basket analysis).
- Understand the business reality behind large sets of data and develop meaningful solutions comprising of analytics as well as marketing management.
- Work closely with internal stakeholders like the business teams, engineering teams and partner teams and align them with respect to your focus area
- Innovate by adapting new modeling techniques and procedures
- 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
- 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.
- Detail-oriented and must have an aptitude for solving unstructured problems. You should work in a self-directed environment, own tasks and drive them to completion.
- Excellent business and communication skills to be able to work with business owners to develop and define key business questions and to build data sets that answer those questions
- Work with distributed machine learning and statistical algorithms to harness enormous volumes of data at scale to serve our customers- 3+ years of data scientist experience
- 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
- Experience applying theoretical models in an applied environment

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