Amazon Data Scientist FinTech - Finance Intelligence 

United States, Washington, Bellevue 

111824808

Key job responsibilities
- Derive novel ML or Computer Vision or LLMs and NLP algorithms. 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.
- Work with very large datasets. Proficiency in both Supervised(Linear/Logistic Regression) and UnSupervised algorithms(k means clustering, Principle Component Analysis, Market Basket analysis).- Design and develop scalable ML solutions. 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.
- Publish your work at major conferences/journals. 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.
A day in the life
In a typical day as a data scientist at Amazon FinTech, you'll begin by delving into massive datasets, applying your technical expertise in feature engineering and exploratory data analysis to uncover valuable insights. You'll utilize both supervised and unsupervised machine learning algorithms, such as linear regression and k-means clustering, to build predictive models and solve complex optimization problems like inventory and network optimization. Collaboration with business, engineering, and partner teams is essential, as you'll translate data-driven findings into actionable insights that align with strategic goals. Throughout the day, you'll innovate by adapting new modeling techniques, ensuring data flow solutions are stable, scalable, and fault-tolerant. Your strong communication skills and attention to detail will help you manage and integrate large datasets, solving unstructured problems and driving projects to completion in a fast-paced, dynamic environment.

- Bachelor's degree
- 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