Americas Head of Data Science / Engineering

We are seeking talented, experienced data engineering and science professionals to join a growing, high-visibility cross-Bank team that is developing and deploying solutions to some of Credit Suisse’s most challenging analytic problems. As a member of this team, the successful candidate will work with clients and data spanning Credit Suisse’s global organization to solve emerging mission-critical challenges via the utilization of emerging technologies such as:



  • Distributed file systems and storage technologies (HDFS, HBase, Accumulo, Hive)

  • Large-scale distributed data analytic platforms and compute environments (Spark, Map/Reduce)

  • Emerging data science techniques and mathematical models (machine learning, graph/link analysis, natural language processing)

  • Tools for semantic reasoning and ontological data normalization (RDF, SPARQL)



Required



  • A formal background and proven experience in mathematics, statistics and computer science, particularly within the financial services sector. Experience within related fields such as physics, chemistry or biology will be considered.

  • A successful history of developing production-quality enterprise software using programming languages such as Python, C, C++, Java or Scala. Experience with R will be considered.

  • The ability to function within a multidisciplinary, global team. Be a self-starter with a solid curiosity for extracting knowledge from data and the ability to elicit technical requirements from a non-technical audience.

  • Solid communication skills and the ability to present deep technical findings to a business audience.

  • 7+ years experience in enterprise IT at a global organization.

  • Fluent written and spoken English.



Additional Skills



  • Experience with distributed computing environments such as Hadoop, Spark or High Performance Computing (HPC) systems and an understanding of parallel application performance and reliability.

  • Experience developing software within a large enterprise environment (agile or associated development methodologies, backtesting, release management, JIRA).

  • An understanding of Unix/Linux including system administration and shell scripting.

  • Database management and business intelligence including SQL, data modeling or schema management. Experience with NoSQL databases such as MongoDB, Cassandra or associated technologies is a plus.

  • An ability to construct end-to-end data analytic workflows including data capture, cleaning, normalization, exploration, modeling and visualization, preferably utilizing the tools previously described.

  • Experience with data science techniques and associated libraries such as NumPy, SciPy, Pandas, NLTK or MatPlotLib (Python) or equivalent R packages. Such techniques include a good to great understanding of statistical models, graph algorithms, probabilistic algorithms, classification, clustering or related approaches as it applies to financial applications.

  • Experience with Semantic Web technologies and methodologies such as OWL, RDF and SPARQL a plus.

  • Investment Banking experience is a plus.

     



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