Job Description
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Company Overview
A leading global technology and mobility organization is building intelligent systems that power large-scale marketplaces and real-world operations. Its Science and Engineering teams work at the intersection of economics, statistics, computer science, and machine learning to develop production-grade technologies that create measurable impact across global platforms.
The organization is seeking a Sr Applied Scientist to work at the intersection of economics, statistics, and computer science to build intelligent systems that support global marketplaces. This production-focused role involves transforming complex behavioral data into scalable, machine-readable insights and automated decision-making systems.
The successful candidate will work in a fast-moving environment where scientific and technical solutions directly influence millions of users and real-world operations. The role requires close collaboration with Product and Engineering teams and ownership of high-visibility projects from conceptualization through global production deployment.
Key Responsibilities
- Build and deploy production-grade machine learning models and statistical algorithms that enhance platform intelligence and user experience in real-time environments.
- Design complex experiments and causal inference frameworks to interpret results and evaluate trade-offs between short-term improvements and long-term system reliability.
- Architect underlying systems, observability platforms, and automated tools required to monitor model performance and identify degradation at scale.
- Translate ambiguous business requirements into rigorous mathematical frameworks and production-ready code.
- Collaborate with Engineering, Product, and Operations teams to influence technical roadmaps and promote scientific best practices.
- Own projects end-to-end, including identifying raw features, addressing data imbalance, developing models, and troubleshooting production issues.
- Develop scalable scientific solutions capable of operating reliably in dynamic, high-impact environments.
- Contribute to production-level codebases and develop reusable tools that can benefit multiple teams.
Required Qualifications
- At least 4 years of professional experience as a Machine Learning Scientist, Research Scientist, Applied Scientist, or in a comparable role involving independent ownership of complex problems.
- Expert proficiency in probability and statistics, including areas such as multivariate distributions and sampling.
- Strong knowledge of core optimization techniques, including Gradient Descent and MCMC.
- Advanced programming skills with the ability to contribute to production-level codebases.
- Experience developing modular and reusable tools for use across engineering or science teams.
- Experience conducting extensive testing, monitoring, and alerting to ensure reliability of real-time systems.
- Demonstrated business acumen and the ability to connect technical decisions to broader strategic business objectives.
- Exceptional written and verbal communication skills, including the ability to create high-impact materials for senior audiences and lead meetings with clear objectives.
- Master’s or Ph.D. degree in Computer Science, Machine Learning, Statistics, Economics, another quantitative discipline, or equivalent professional experience.
Preferred Qualifications
- Deep expertise in developing large-scale intelligent systems involving supply, demand, user behavior, or other dynamic environments.
- Experience with Bayesian methodologies and probabilistic programming frameworks such as STAN or Pyro.
- Experience with advanced reinforcement learning techniques.
- Demonstrated ability to lead cross-functional projects in highly ambiguous and rapidly changing environments.
- Strong ownership and resilience with the ability to deliver high-quality solutions under demanding timelines.
- Commitment to engineering excellence and production reliability.
- Ability to independently identify opportunities, define solutions, and drive projects from concept through implementation.
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