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 Amazon's 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 team's 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.
About The Team
The MIDAS team operates within Amazon's Digital Analytics (DA) engineering organization, building analytics and data engineering solutions that support cross-digital teams. Our platform delivers a wide range of capabilities, including metadata discovery, data lineage, customer segmentation, compliance automation, AI-driven data access through generative AI and LLMs, and advanced data quality monitoring. Today, more than 100 Amazon business and technology teams rely on MIDAS, with over 20,000 monthly active users leveraging our mission-critical tools to drive data-driven decisions at Amazon scale.
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.
Preferred Qualifications
5+ years of data scientist experience
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.
Company - ADCI MAA 15 SEZ
Job ID: A3205211

Work arrangement
No