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 data processing needs.
- And strong skills in methodology, data visualisation, deployment method, industrialisation tools, etc.
Attitude
- Proactively invests time in continuous learning and knowledge improvement.
- Demonstrates awareness of efficiency and efficacy.
- Thinks out of the box outside existing processes and frameworks.
- Works with energy and empowerment to deliver great results and a large contribution to the company’s success
- Is constructive by being open to the changes and to other’s opinions, ideas and feedback
The Cognizant community:
We are a high caliber team who appreciate and support one another. Our people uphold an energetic, collaborative and inclusive workplace where everyone can thrive.
- Cognizant is a global community with more than 300,000 associates around the world.
- We don’t just dream of a better way – we make it happen.
- We take care of our people, clients, company, communities and climate by doing what’s right.
- We foster an innovative environment where you can build the career path that’s right for you.
About us:
Cognizant is one of the world's leading professional services companies, transforming clients' business, operating, and technology models for the digital era. Our unique industry-based, consultative approach helps clients envision, build, and run more innovative and efficient businesses. Headquartered in the U.S., Cognizant (a member of the NASDAQ-100 and one of Forbes World’s Best Employers 2024) is consistently listed among the most admired companies in the world. Learn how Cognizant helps clients lead with digital at www.cognizant.com.
Our commitment to diversity and inclusion:
Cognizant is an equal opportunity employer that embraces diversity, champions equity and values inclusion. We are dedicated to nurturing a community where everyone feels heard, accepted and welcome. Your application and candidacy will not be considered based on race, color, sex, religion, creed, sexual orientation, gender identity, national origin, disability, genetic information, pregnancy, veteran status or any other protected characteristic as outlined by federal, state or local laws.
Disclaimer:
Compensation information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.
Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government issued ID during each interview.
While our system allows application in all languages, job required language(s) and proficiency level(s) vary. However, basic English proficiency is required for Company-wide communications purposes.