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Big Data Engineer - (Python / PySpark)

Location: Zürich, Switzerland
Sector: Java
Job Type: contract
Salary: Negotiable
Reference: BBBH143614

Experis is the global leader in professional resourcing and project-based workforce solutions. Our suite of services ranges from interim and permanent recruitment to managed services and consulting, enabling businesses to achieve their goals. We accelerate organisational growth by attracting, assessing, and placing specialised professional talent.

Key Responsibilities

As a Data Scientist Backend Developer, you will be assigned to the CRO Data & Reporting Foundation program, working with the rest of the team on implementing core infrastructure functions for data sourcing, processing (incl. DQ controls, data adjustments, etc.) on the strategic infrastructure, mostly relying on the Foundry platform from Palantir as formerly deployed in the Bank for Compliance.

The role involves:

  • Close engagement with CRO Change project teams and RDM BAU team to collect requirements for needed infrastructure capabilities for implementing fully functional CRO reporting applications and enabling digitalized BAU processes, while complying with regulatory rule set for Risk Reporting
  • Supporting a dedicated squad team on specifying user stories and managing the backlog for implementation; implementing solutions, as well as performing UAT of provided solutions
  • The opportunity to become part of our highly motivated Analytics team focusing on building and driving new technologies in various fields
  • A challenging role as a Data Scientist in a dynamic, international and fast paced environment using the latest innovations in predictive analytics and visualization techniques
  • The chance to engage with senior Business users in automating regulation and compliance to build a state-of-the art regulatory reporting and analytics infrastructure
  • You will develop and design algorithms, build prototype versions, run multiple validations with business experts and work on building products

Essentials Skills and Qualifications:

  1. At least 8 years of the overall experience and 4 years with the coding at least one of the following: Python, Java/Scala, SQL and/or SAS
  2. PhD or Master Degree in Computer Science or Data Science or a quantitative field (Statistics, Mathematics, Economics).
  3. Demonstrated ability in applying statistical modelling, machine learning and / or exploratory analysis to large datasets: classification, scorecard models, segmentation, clustering, Bayesian statistics, anomaly detection, NLP
  4. Proficient in at least one of the following: Python, Java/Scala, SQL and/or SAS
  5. Experience with relational database programming and distributed data processing at scale using Spark or/and Hive
  6. Fluency in English (written and spoken); German or French would be a plus
  7. Your high-level of confidentiality, integrity and responsibility to deal with critical and confidential data
  8. You feel at ease in assembling data sets from disparate sources and analyze using appropriate quantitative methodologies, computational frameworks and systems

Desired Skills and Qualifications:

  • Your excellent people management abilities and good communication skills that enable you to collaborate effectively and to build up professional relationships even with senior level executives
  • As demonstrated standout colleague, self-starter, and independent thinker you are willing to cooperate in a highly collaborative environment and to contribute to the team's success
  • You are willing to assume additional responsibilities and to become a respected know-how carrier in our team

Interested in this opportunity? Kindly send us your CV today through the link in the advert. However, should you have any questions please contact Danny Besse on +41 44 229 99 45.

Even though this position may not be the perfect fit for you, please reach out to us, as we have hundreds of open positions at Experis IT across Switzerland.

Check out all of Experis' job openings at or visit my personal page and connect to me on LinkedIn.

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