Senior Associate
il y a 1 mois
**Job Title**: Data scientist
**Mission**:
The Data Scientist plays a critical role as main actor for Data Exploitation focused on Analytics but also contributes to Data architecture and management.
he goal of the Data Scientist is to extract new information from data (insights or predictions) with which the Business can generate value.
The Data Scientist also contributes to the definition and the implementation of the software strategy, and to the evolution of the A&AI environment.
The function of Data Scientist covers two related roles, one “model-oriented” and one “deployment-oriented” (described below).
The common skills (hard skills - mathematics and modelling - and soft skills) are described below, as well as the skills specific to deployment.
Note that the efficiency of a team depends on the combination of various qualitative profiles. It is obviously not frequent to master both aspects simultaneously.
**Principal assignments**:
**Common skills**
- Business Strategy: it is expected that the Data Scientist puts in perspective her/his analysis with the business context; it is a constant concern to make the link between the “figures” and “reality”.
- Customers: the Data Scientist is in contact with officers from the domain requesting the analysis; normally there is no contact with external clients; the Data scientist interacts effectively with the Business Stakeholders to understand the business opportunity/challenge and how new information can make a difference; deliver the results to the end user in a way that the buy-in is successful; the Data Strategist can facilitate on this point if necessary;
- Data Exploitation: the Data Scientist can effectively map the business context into the data and define the analytical approach; locate the required data in the data architecture and assemble it for analysis; execute the analysis to obtain the required information (this involves data exploration, feature engineering, modelling and evaluation);
- Data Management: the Data Scientist is the power user of Data; s/he plays a key role in improving the data management practice of the organisation, by contributing to the enrichment of Metadata, advising for improvements in the Enterprise Information Model and the Data Architecture, and warning about Data Quality problems; the Data Scientist is also responsible to document the models he made in a transparent and understandable way.
- Coaching: as the Data Scientist progresses in the career, s/he has the responsibility for coaching less experienced colleagues and contributes to the steering of collaboration with external partners
- Risk & Compliance: the Data Scientist is the last link in the chain to make sure that all data processing is performed under a valid legal basis; additionally, the Data Scientist has the duty of nondisclosure about any customer information at a nominal level. The Data Scientist, as user of data, respects all regulatory rules and follows the guidelines as defined by the bank. (incl. GDPR,)
- Change Management: The Data Scientist supports change management by providing any additional work still required to deploy the results and make them accessible to the end user.
**Deployment-oriented aspects**:
- Industrialization & Clients: the Data Scientist is in contact with Data Scientist(s) from the department that has created the model - if s/he has not created the model him/herself. The Data Scientist must understand the context and the challenges faced by the Business and propose a way to industrialize it in an optimal way to guarantee the added value of the deployed model(s)
- Data and IT Architecture: the Data Scientist understands the business context related to the data analysis and includes it in his/her deployment approach. He/she knows the IT architecture, the different data sources and their location in the system. He/she oversees the documentation of the model industrialization in a transparent and understandable way.
- Risk & Compliance: the Data Scientist must ensure that the deployed models, the information flows and the automatic delivery of the results are done on a valid legal basis. As responsible of the data flow automation, data transformation and calculation, he/she respects all the rules and follows the regulations defined by the Bank (e.g. privacy and ethical directives)
**Experience in the relevant domain**:
**Techniques**
- A&AI languages and Tools: R/RStudio, SAS
- Programming languages Python, Spark, scripts Ansible, SQL, Scala (optional), Kafka, (optional); Flink (optional)
- Development Tools & Libraries: Conda, GitLab, Jupyter, PyCharm,
- Deployment tools like Nexus, Kubernetes, Docker, Jenkins, cdd, RA, Control-M (batch mode), Fortify&SonarCube
- Machine Learning: applies effectively different algorithms with a good usage of parameters available
- Master mathematics, probability and statistics
- Data Processing: collaborates with Data Engineers to optimise database engines accordingly to da
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