Data Scientist II — Financial Crime Analytics

RMB Nigeria Limited; Johannesburg; On-site; ZAR 597,000 - 686,000; Full time

Benefits offered by this job

Opportunities to network and Collabor-

Challenging Work

Opportunities to innovate

Job summary

FNB in Johannesburg is seeking a Data Scientist II to plan, build, and implement quantitative models for financial crime analytics (AML/CFT, transaction monitoring, sanctions screening). You will translate complex results for non-technical stakeholders and support governance.

Ideal candidates hold an Honours degree in a related field and 2+ years of hands-on experience with SAS, SQL, and Python. Collaboration across risk, business, and technology teams is essential, as is documenting models and

Qualifications

• Hold an Honours Degree in Mathematics, Statistics, Computer Science, Actuarial Science, Engineering, Physics, or a related field.
• Have 2+ years of hands-on experience in developing and implementing analytical models, preferably in financial services.
• Possess strong scripting skills in SAS, SQL, and Python.
• Experience in financial crime, fraud detection, or AML/CFT is a strong advantage.

Responsibilities

• Deliver on model development, implementation, and governance for financial crime risk analytics (AML/CFT, transaction monitoring, sanctions screening).
• Proactively identify analytical opportunities and solve unstructured problems with minimal guidance.
• Translate technical solutions into actionable insights for non-technical stakeholders.
• Deliver clear and comprehensive technical documentation for models and processes.
• Collaborate with business, risk, and technology teams across the FirstRand group to drive model effectiveness and compliance.
• Support junior Data Scientists in developing both technical and business skills.

Skills

SAS

SQL

Python

Collaboration

Education

Honours Degree in Mathematics/Statistics/CS/Actuarial/Engineering/Physics

Job description

FNB in Johannesburg is seeking a Data Scientist II to plan, build, and implement quantitative models for financial crime analytics (AML/CFT, transaction monitoring, sanctions screening). You will translate complex results for non-technical stakeholders and support governance.

Ideal candidates hold an Honours degree in a related field and 2+ years of hands-on experience with SAS, SQL, and Python. Collaboration across risk, business, and technology teams is essential, as is documenting models and