• Competitive
  • San Francisco, CA, USA
  • Festanstellung, Vollzeit
  • Credit Suisse -
  • 17 Jan 19

Credit Suisse Labs Senior Member of the Technical Staff

We Offer
The CS Labs Mission:

Help Credit Suisse explore and build its future. Show the power of small autonomous teams of engineers and scientists working together towards clear common goals. Leverage new tools and new ways of working to build new businesses and 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 great things, and we have an amazing organization to help us.

Behavioral Profile

You are insatiably curious with an insurmountable appetite for learning. While understanding the desired outcome of a venture you find yourself compelled to fully comprehend and question its purpose. You impart with skill what you have learned and can clearly explain the technical to the non-technical. 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 brave enough to challenge the status quo while your imagination, insight and experience compel you to speak up when you know changes can be made for the better. You have the confidence to perturb the old ways of thinking to bounce systems and processes out of local minima. You strive to do so with courtesy and understanding, but never become a slave to the nuances of an organization's culture. A reputation of successfully solving problems, following through on promises and delivering above expectations when others are in doubt, precedes you

You are someone who thinks as much about the posing of the problem as the clever solution to it, and while your tenacity makes it tough to give up on a challenge, your higher reasoning tells you when it's time to quit.

Engineer / Scientist Profile

In the role of member of technical staff you are required to engage with the business, gather data, form and test hypotheses, as well as build, deploy and demonstrate PoCs. The ideal candidate will have a background in finance with at least five years of professional experience in roles combining computer science, data science and software engineering.

You Offer
You can easily express your ideas in code and are an expert in:

  • Scala, Clojure, Go, C or Java
  • Python or R

You have led projects throughout the full software development life cycle and are well versed in:

  • Agile, including scrum and pair programming
  • Test-driven development
  • Prototyping
  • Specifying requirements and estimating effort
  • Deployment and scaling out to cloud platforms such as AWS and GCP

As a practitioner in the art of transformation and exploration of very large data sets, you have deep knowledge in most of the following:

  • Information Retrieval, e.g. Elasticsearch
  • Distributed computing including Hadoop and Spark
  • Distributed Streaming such as Kafka and Kinesis
  • SQL and NoSQL databases such as Hive, Redshift, HBase and Cassandra

You have a repertoire of algorithms and tools for exploratory data analysis such as:

  • Clustering and dimension reduction
  • Classification
  • Recognition and recommendation systems
  • Building visualizations and dashboards

Additionally, you are a proponent of the DevOps movement and therefore knowledgable in:

  • Application Containers
  • Kubernetes
  • Orchestration
  • CI/CD
  • Network architecture
  • Application monitoring such as Graphite, Grafana and New Relic
  • Security

A background in finance with emphasis on the following would be ideal:

  • Financial instruments including swaps, options, futures and forwards
  • Modern portfolio theory
  • Market and reference data composition and sources

Potential Deep Areas of Expertise

In addition to the broad background described above, you have one or more deep areas of subject matter expertise. Specific subject-matter expertise that interests us include (but are not limited to):

  • DevOps
  • Functional programming
  • Exploratory data analysis
  • Deep learning
  • Design thinking