- San Francisco, CA, USA
- Permanent, Full time
- Credit Suisse -
- 25 Apr 18
Credit Suisse Labs Member of the Technical Staff - AI & Machine Learning or Big Data and Data Engineering #108502
(Please see other job descriptions for Functional Programming, Platform Engineering, Design Strategist, and Social Scientist specializations)
The CS Labs Mission:
Help Credit Suisse explore and build its future. Show the power of small autonomous teams of craftsmen and scientists working together towards clear common goals. Use new tools and new ways of working to build new businesses or refresh old ones in a way that benefits both Credit Suisse as well as society as a whole.
The CS Labs are a new entity within Credit Suisse, a leading global bank. We are in downtown San Francisco. We intend to do extraordinary things, and we have an amazing organization to help us do them.
You are insatiably curious, a disciplined free thinker with an EQ high enough to learn from and teach people who see the world differently to how you do. You understand, but never become a slave to, the nuances of an organization's culture. You have profound mathematical and algorithmic intuition, and are used to mastering anything you set your mind to, fast, whether with or without someone to teach you. At the same time you have a gift for explaining the technical to the non-technically minded. Indeed, you are a natural teacher and diffuser of knowledge, and so your colleagues are constantly learning from you (as you do from them). You are someone who thinks as much about the posing of the problem as the creative solution to it, and while you have a drive towards results and completion, you also practice non-attachment as problems and projects change, evolve … and sometimes die. You work as well within teams as with other teams - people want to work with you. You are open-minded, warm, courteous, courageous and optimistic. The power of computing and how it will impact the world, but you have remained a humanist, concerned above all with the human condition: your finely-calibrated moral compass shows you the way.
Hacker / Craftsman / Engineer Profile
You dream in code, and you code all the time, for work and (ideally) for fun. You have mastered at least one enterprise or systems language (C++, Java, Scala, Go, Clojure, etc.) as well as a scripting language or DSL (Python, R, Ruby, etc.) You use modern development tools like GitHub, git, docker, and you are very comfortable with cloud computing (AWS or GCP). You are practiced in collaborative programming paradigms like agile or pair programming. You are a craftsman about how you treat code and have strong opinions about things like: test driven development, design by contract, separation of concerns, object orientation or functional programming. You are able to demonstrate this with public GitHub repos, publications, patents or other projects on which you have worked. You have a strong sense of what constitutes technical debt and how to minimize its accrual. While you compulsively craft code that is modular, clear and generally easy for other specialists and scientists to use, you are always mindful of the underlying algorithms - you don't just glue APIs together but can go as deep as necessary when needing to build new functionality.
Potential Deep Areas of Expertise
In addition to the broad engineering background described above, you have one or more deep areas of domain expertise. Specific subject-matter expertise that interests us includes (but are not limited to):
AI & Machine Learning
You have extensive and proven knowledge within the domain of machine learning. You have a repertoire of algorithms, from clustering and dimension reduction through classification, recognition and recommendation systems, with the aptitude for how and when to combine and apply them given specific problems and datasets. Your curiosity has taken you beyond an understanding of how to apply the algorithms to compel you to know their inner workings. Back-propagation, stochastic gradient descent, matrix algebra, numerical methods and their optimization will be well-worn and familiar tools. Your experience in real-world, production ML applications has made you adept in the full process of problem statement, data acquisition and conditioning through to feature engineering, training, modeling, validation, optimization and deployment. You have used modern frameworks such as TensorFlow, Keras and Caffe2 to train models ultimately deployed to a production environment. You may also have experience in cognitive computing tasks such as dialogue systems.
Big Data and Data Engineering
At the intersection of technology and data in all its forms, you seek and see chances to create services that bring transparency to complex business environments and products that empower customers to better understand and improve their outcomes. You have experience building out and scaling modern data pipelines and platforms. On top of these platforms you have engineered applications that blend unstructured and structured datasets that deliver transformational value to business operations and/or customers. You are part engineer and part data scientist.
Experience and skills should include:
- Big data tools like Hadoop, Spark, and Kafka
- Either AWS cloud services EC2, EMR, Redshift or Google Cloud Platform
- Both SQL and NoSQL (e.g. Cassandra, HBase) databases
- Data science / EDA skills and a strong statistical background
- Experience with Search/IR technologies like Elastic Search a huge plus