Data Scientist

Samaha Consulting; Johannesburg; On-site; ZAR 600 000 - 800 000; Full time

Job summary

A leading consulting firm in Johannesburg is seeking an experienced Data Scientist to drive analytics and AI initiatives that support business outcomes. The ideal candidate will have at least 5 years of experience in data science, specializing in AI/ML models within the banking or financial sector. Responsibilities include developing models, utilizing big data, and providing insights to enhance decision-making processes. A relevant degree and expertise in Python, Hadoop, and data engineering are required for this role.

Qualifications

• 5+ years relevant work experience as a Data Engineer/Data Scientist.
• Experience in AI/ML models in banking or financial services.
• Proficient in open-source languages like Python and R.

Responsibilities

• Drive analytical interpretation of data for business results.
• Lead initiatives for AI and big data in business contexts.
• Develop and deploy advanced models for strategic decisions.

Skills

Data analysis

Machine learning

Programming in Python

Statistical analysis

Big data technologies

Agile/DevOps practices

Education

Minimum of 4-year degree in Computer Science, Mathematics, Statistics, Data Science or related field

Master's Degree in Data Science, AI/ML, or related field (preferred)

MBA or Masters is advantageous

Tools

Python

Hadoop

Apache Spark

Jupyter Notebook

GitHub or GitLab

Job description

Responsibilities

• The Data Scientist is responsible for driving the analytical, statistical, and programming interpretation of data to support decision making and drive business results.
• The Data Scientist supports product, teams with insights gained from analysing company & customer data to provide business predictions, proposals, and recommendations to improve business outcomes.
• Drive measurable business outcomes by turning data into automated decisions by building active tools triggering an automated action.
• To lead and provide expertise on Big data and AI initiatives, analysing business requirements and designing appropriate models and ensuring application architecture alignment.
• Translate business strategy into AI use cases that drive measurable value (e.g., revenue growth, efficiency gains, risk reduction).
• Utilize advanced algorithms and analytics to manage large transactional wallet datasets, optimizing profitability through precise risk assessment & strategic decision-making
• Develop and deploy advanced models and algorithms, ensuring robust and accurate risk assessment to support effective management strategies
• Integrate AI seamlessly with existing data, BI, and operational platforms.

Education

• Minimum of 4-year tertiary degree in Computer Science, Mathematics, Statistics, Data Science or related field
• Master’s Degree in a Data Science, AI/ML, Statistical or related field (preferred)
• MBA or Masters (advantageous)

Experience

• 5 or more years of relevant work experience as a Data Engineer/ Data Scientist
• 4–6+ years applied data science; 2+ years owning production AI/ML.
• At least 3 years’ experience within a non-traditional FinTech, Banking or Financial Services Sector
• Experience in Data Science and Data Analysis with a specific focus on AI/ ML models within banking, finance and/or telecommunications industry
• Proven delivery of automated decisioning (recommendation/propensity/fraud/forecasting) with quantified business impact.
• Experience in Data Engineering within banking or financial services industry
• Understanding of enterprise-scale systems and technologies used in data infrastructures
• Experience of working in an Agile/DevOps environment
• GitHub or GitLab experience for CI/CD
• Proficient in working with open-source languages such as Python, Jupyter Notebook, R / Spark - Scala and others to drive optimized data engineering and machine learning best practice frameworks
• Working knowledge in Hadoop, Apache Spark and related Big Data technologies and their applications in data engineering and MLOps pipelines
• AI/ Machine learning for predictive modelling and other relevant use cases
• Understanding of FinTech, banking, microfinance and payment businesses