In this role, you will have the opportunity to invent and implement novel scientific approaches that address critical business problems at the product level. You will collaborate closely with product managers, engineers, and business stakeholders to translate scientific innovations into practical, scalable solutions that enhance AppSense's capabilities and deliver value to our customers.
Key job responsibilities
* Lead the design, implementation, and delivery of scientifically complex solutions for AppSense, focusing on areas such as automated software discovery, intelligent cost optimization, and predictive analytics
* Develop and apply state-of-the-art machine learning models to improve software categorization, usage prediction, and anomaly detection
* Create innovative natural language processing solutions for contract analysis, optimization, and automated report generation
* Design and implement advanced recommendation systems for software stack optimization based on job roles and team compositions
* Develop reinforcement learning algorithms for automated license management, including predictive maintenance to prevent unexpected expirations or overage charges
* Develop AI-driven negotiation assistants and collaborative budgeting tools with ML-powered spend forecasting
* Create sentiment analysis models to gauge software satisfaction from user feedback and support tickets
About the team
The AppSense team is at the forefront of transforming software asset management at Amazon. We're building a comprehensive platform that provides visibility, control, and optimization for Amazon's vast software portfolio. Our mission is to leverage cutting-edge technology to help businesses discover, manage, and optimize their software assets, driving significant cost savings and operational efficiencies.
As part of the applied science team within AppSense, you'll work alongside talented scientists, engineers, and product managers who are passionate about solving complex problems at scale. We foster a culture of innovation, encouraging team members to push the boundaries of what's possible in software asset management. Your contributions will directly impact Amazon's bottom line and have the potential to shape the future of how organizations manage their software ecosystems.
BASIC QUALIFICATIONS
- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
PREFERRED QUALIFICATIONS
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability
- Experience using managed ML/AI solutions
- Strong skills in problem solving, programming, and computer science fundamentals
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.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $150,400/year in our lowest geographic market up to $260,000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.