Data Scientist II, FinOps - Global Finance Solution

Data Scientist II, FinOps - Global Finance Solution

Amazon is a US-based multinational electronic commerce company headquartered in Seattle, Washington. Amazon.com started as an online bookstore, but soon diversified into many other categories, with a vision to be earth's most customer-centric company & to build a place where people can come to find and discover anything they might want to buy online.

About the role: Amazon's Global Finance Solutions (GFS) team is a fast-paced, team-focused, dynamic environment and delivering great experiences for our customers is top priority. GFS is seeking a Data Scientist with the technical expertise and business intuition to invent the future of Finance at Amazon. The role lead the research and thought leadership to drive our data and insight strategy for Finance. You will be expected to serve as a Full Stack Data Scientist. You will be responsible for driving data-driven transformation across the organization. In this role, you will be responsible for the end-to-end data science lifecycle, from data exploration and feature engineering and ETL to model development. You will leverage a diverse set of tools and technologies, including SQL, Python, Spark, Hugging Face and various machine learning frameworks, to tackle complex business problems and uncover valuable insights.

Your product analytics research will provide direction on the technology strategy of the Managed Operations organization. Your Decision Science artifacts will provide insights that inform Finance Operations team. You will work on ambiguous and complex business and research science problems at scale. You are and comfortable working with cross-functional teams and systems.

Key job responsibilities
The Data Scientist's responsibilities include, but are not limited to the following points:
- Extract and analyze large amounts of data related to suppliers and associated business functions.
- Adapt statistical and machine learning methodologies for Finance Operations by developing and testing models, running computational experiments, and fine-tuning model parameters.
- Use computational methods to identify relationships between data and business outcomes, define outliers and anomalies, and justify those outcomes to business customers.
- Communicate verbally and in writing to business customers with various levels of technical knowledge, educate stakeholders on our research, data science, and ML practice, and deliver actionable insights and recommendations
- Develop code to analyze data (SQL, PySpark, Scala, etc.) and build statistical and machine learning models and algorithms (Python, R, Scala, etc.).
- Collaborate with business and operational stakeholders and product managers to innovate on behalf of customers, develop novel applications data science methodologies, and partner with engineers and scientists to design, develop, and scale machine learning models.

BASIC QUALIFICATIONS

- 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
- 5+ years of data scientist experience
- 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
- Bachelor's degree
- Experience applying theoretical models in an applied environment

PREFERRED QUALIFICATIONS

- Experience in Python, Perl, or another scripting language
- Experience in a ML or data scientist role with a large technology company

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 https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

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