Do you want to be part of a team dedicated to building machine learning applications that process billions of dollars a day using the latest technology?
In Finance Technology, we build machine learning systems that look through billions of dollars in transactions from across the company, and build systems that include regression, classification, clustering, and anomaly detection system. This means you can work with different technologies and business problems ranging from finding anomalies in data all the way to predicting how much cash to keep in an account.
In a typical day, you will work with other machine learning scientists, software engineers, and business groups. You will have access to terabytes of data, and will use that to develop sophisticated models and insights. We partner with our customers, so you will meet with them directly to gain direct feedback on your results and insights. Our team and customer are comfortable trying new ideas, so you will test your ideas in the real world.
Discover actionable insights from large volumes of data.
Investigate the feasibility of a scientific principle or concepts to business problems.
Use code (Python, Scala, SQL, etc.) to analyze data and build statistical and machine learning models and algorithms.
Combine statistics, NLP and machine learning techniques to create scalable solutions for business problems.
Partner with developers and business teams to test your models in production.
Build customer-facing reports explaining your insights
Work closely with business staff to propose and build solutions.
· Bachelor's Degree
· 3+ years of experience with data scripting languages (e.g SQL, Python, R etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab)
· 2 years working as a Data Scientist
· 5+ years of work experience in an role involving data extraction, analysis, and communication.
· Experience with causal inference, NLP, or anomaly detection
· In-depth knowledge on supervised and unsupervised machine learning algorithms including classification, clustering, and regression.
· Expertise in Python. Skilled in manipulating and processing data using libraries such as Scikit-learn, Pandas, and NumPy.
· Demonstrated experience in SQL and/or NoSQL data modeling.
· Experience processing, filtering, and presenting enormous quantities of data. Experience building complex data visualizations.
· Ability to distill ambiguous customer requirements into a science objective.
· Ability to explain mathematical concepts and considerations to non-experts.
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.