Applied Scientist 2
2 days ago
What if your job description were simply “make tomorrow better?” Every day at Microsoft, we bring an insatiable curiosity to the workplace, challenging ourselves to reimagine what it is and what it can be. We build on what’s come before to create what’s next. We help shape the future and we empower billions of people around the globe.
We are a team of applied scientists working on machine learning components in the whole sponsored search stack. Our team works on problems related to machine learning, deep learning, natural language processing, image understanding, optimization, information retrieval, auction theory, among others. Our work entails building large-scale machine learning systems for ad matching, filtration, ranking, and multiobjective optimization, and a number of other ML-driven business problems.
**Responsibilities**:
This specific position is specially intended towards Optimizations, MonteCarlo Simulations, Multi-arm bandits, Game theory, Auction theory and Ranking and Pricing. You will design, implement, analyze, tune complex algorithms and ML systems and the supporting infrastructure for operating on large datasets. You will collaborate with top machine learning scientists and engineers in delivering direct business impact. We're looking for sound understanding and insight into productionizing machine learning models in large-scale systems, an ability to pick up new technical areas, as well as a commitment to developing, delivering, and supporting algorithms in production.
**Qualifications**:
- MS/BS in CS/EE, mathematical or machine learning related disciplines, with 3 or more years of experience
- Solid understanding of probability, statistics, machine learning, data science
- A/B testing & analysis of ML models, and optimizing models for accuracy
- Experience with Hadoop, Spark, or other distributed computing systems for large-scale training & prediction with ML models.
- End-to-end system design: data analysis, feature engineering, technique selection & implementation, debugging, and maintenance in production.
- Experience implementing machine learning algorithms or research papers from scratch
- Experience with TensorFlow/PyTorch and deep learning models
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