Intermediate Data Scientist (Johannesburg)

A long-standing, well-established bank is seeking a for a highly analytical and proactive Intermediate Data Scientist to join their team. The ideal candidate will have strong expertise in working with structured and unstructured data, developing and deploying AI/ML solutions, and delivering actionable insights. This role requires an individual capable of independently identifying opportunities in data and taking end-to-end ownership of analytical projects.

Responsibilities:

• Develop, test, and deploy ML models (linear, 3-phased, deep learning) for real-time and batch analytics.
• Work with unstructured data sources, including location data, images, voice notes, and text.
• Conduct data mining to identify trends, patterns, and opportunities upfront.
• Design and maintain dashboards (R Shiny, Dash, Power BI) for internal and stakeholder reporting.
• Implement and monitor Responsible AI and ML Ops solutions for model governance.
• Utilize big data technologies such as Hadoop, Spark (Python and R), I-Spark, and Kafka for processing and streaming large datasets.
• Work with cloud platforms, preferably AWS, for data storage, processing, and analytics.
• Collaborate with cross-functional teams and communicate findings effectively to technical and non-technical stakeholders.
• Manage high-pressure projects with strict deadlines while ensuring data quality and accuracy.

Qualifications and Experience:

• Honours degree in a related field (statistics, computer science, data science, mathematics, or equivalent)
• Strong analytical skills with a track record of translating data into actionable insights.
• Proficient in Python, R, and SAS.
• Hands-on experience with Spark, Hadoop, Kafka, and real-time data streaming.
• Experience with ML Ops and deploying AI-based solutions end-to-end.
• Ability to work independently and proactively identify data opportunities.
• Strong communication and presentation skills.
• Familiarity with confluence or similar collaboration/documentation tools.
• Experience with dashboards and visualization tools (R Shiny, Dash, Power BI).
• Demonstrated evidence of deployed AI/ML solutions in production