For those interested in Big Data and/or Machine Learning ...
If you only want to experiment with the latest ML models and spend months tuning features and hyperparameters until model A outperforms model B, then applied research may be a better fit.
However, if you want the full responsibility of building out a product which uses machine learning to provide key customer insights to over 250 multi-billion dollar corporations across the world, then consider joining Medallia's text analytics team.
What we're actually working on:
- debugging performance bottlenecks in distributed data set clustering algorithms
- building clean data model abstractions to support an interactive topic modeling application
- gauging per-client user-impact when changing various pieces of our text processing stack
- breaking a monolithic architecture into microservices to handle new exciting use cases!
- porting word2vec to java
If this sounds like fun, then please send an email to [email protected]
Medallia Analytics Engineer - Palo Alto CA ML, IR/search,
Distributed Processing
Come work on feedback analytics at Medallia, a Palo Alto-based tech company. Our company's mission is to create a world where businesses are loved by their customers.
My team specifically works on problems like sentiment analysis and topic classification to extract insights from feedback data such as comments on review sites, transcribed tech support calls, suggestions boxes left at your favorite restaurant, etc.
We've built an 85%+ accurate sentiment classifier which works in six languages, open sourced a Java port of Word2Vec, and created a semi-supervised topic clustering algorithm.
If you're interested in applying distributed systems, NLP, and ML to help businesses improve, please contact Andrew Ko at [email protected]
My team works on analytics at Medallia, a Palo Alto-based company. Our company's mission is to connect businesses with their customers. My team specifically works on problems like sentiment analysis, topic classification, outlier detection, etc to derive insights from feedback including comments from hotel review sites, transcribed tech support calls, suggestion boxes left at your favorite restaurant, etc.
We've built an 85%+ accurate sentiment classifier which works in six languages, open sourced a Java port of Word2Vec, optimized a language detector for our data, and are currently working on a semi-supervised topic clustering algorithm. In addition to the research component, our team also handles scalability and real-time distributed processing, processing millions of comments per hour.
Hi! My team works on analytics at Medallia. Our company's mission is to connect businesses with their customers. My team specifically works on sentiment analysis, topic classification, outlier detection, etc to derive insights from feedback including comments from hotel review sites, transcribed tech support calls, suggestion boxes left at your favorite restaurant, etc.
We've built an 85%+ accurate sentiment classifier which works in six languages, open sourced a Java port of Word2Vec (https://github.com/medallia/Word2VecJava), optimized a language detector for comment data, and are currently working on a semi-supervised topic clustering algorithm. In addition to the research component, our team also handles scalability and real-time distributed processing, processing millions of comments per hour.
If you are passionate about applied machine learning and want to impact the way businesses interact with people, please reach out to me at [email protected] :)
If this sounds way too ML-oriented but you're still curious, fear not! We're also doing some pretty neat stuff with:
For those interested in Big Data and/or Machine Learning ...
If you only want to experiment with the latest ML models and spend months tuning features and hyperparameters until model A outperforms model B, then applied research may be a better fit.
However, if you want the full responsibility of building out a product which uses machine learning to provide key customer insights to over 250 multi-billion dollar corporations across the world, then consider joining Medallia's text analytics team.
What we're actually working on:
- debugging performance bottlenecks in distributed data set clustering algorithms
- building clean data model abstractions to support an interactive topic modeling application
- gauging per-client user-impact when changing various pieces of our text processing stack
- breaking a monolithic architecture into microservices to handle new exciting use cases!
- porting word2vec to java
If this sounds like fun, then please send an email to [email protected]