Data Scientist, Digital Acceleration

DESCRIPTION

Are you excited about the digital media revolution and passionate about designing and delivering advanced analytics that directly influence the product decisions of Amazons digital businesses. Do you see yourself as a champion of innovating on behalf of the customer by turning data insights into action
The Amazon Digital Acceleration Analytics team is looking for an analytical and technically skilled individual to join our team. In this role,
you will invent, build and deploy state of the art machine-learning models and systems to enable and enhance the teams mission
This role offers wide scope, autonomy, and ownership. You will work closely with software engineers & data engineers to put algorithms into practice. You should have strong business judgement, excellent written and verbal communication skills. The candidate should be willing to take on challenging initiatives and be capable of working both independently and with others as a team.
Key job responsibilities
We are looking for an experienced data scientist with strong foundations in mathematics, statistics & machine learning with exceptional communication and leadership skills, and a proven track record of delivery. In this role, You will

Define a long-term science vision and roadmap for the team, driven fundamentally from our customers needs, translating those directions into specific plans for engineering teams.
Design and execute machine learning projects/products end-to-end: from ideation, analysis, prototyping, development, metrics, and monitoring.

Drive end-to-end statistical analysis that have a high degree of ambiguity, scale, and complexity.
Research and develop advanced Generative AI based solutions to solve diverse customer problems.

BASIC QUALIFICATIONS

- 2+ 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
- Experience applying various machine learning techniques, and understanding the key parameters that affect their performance. . Experience developing experimental and analytic plans for data modeling processes, use of strong baselines, and the ability to accurately determine cause and effect relationships. . Have a history of building systems that capture and utilize large data sets in order to quantify performance via metrics or KPIs. . Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc.