Senior Data Scientist at Nedbank

Job Purpose

• Apply domain-specific expertise in machine learning, data mining, and information retrieval to architect and build highly specialized and advanced analytic engines and services, pushing the boundaries of knowledge in the field and providing expert guidance to the enterprise.

Job Responsibilities

• Design and develop high-quality, reusable features for business-critical ML models.
• Apply advanced machine learning techniques (e.g., supervised, unsupervised, deep learning) to solve complex business problems.
• Conduct data discovery and exploratory analysis to identify valuable features and patterns.
• Contribute to the feature store lifecycle, including documentation, versioning, and governance.
• Apply graph-based techniques to model relationships and extract features for downstream ML tasks.
• Monitor and continuously improve deployed models, ensuring performance, fairness, and ethical compliance.
• Implement design of experiments, hypothesis testing, and model validation strategies.
• Build and maintain scalable, production-grade data pipelines for feature computation and model training.
• Transform raw data into clean, structured, analytics-ready datasets using modern data engineering tools (e.g., dbt, Airflow, Spark).
• Engineer real-time and batch data workflows to support ML and analytics use cases.
• Collaborate with other data engineers and scientists to ensure seamless integration of features into model pipelines.
• Implement data quality checks, monitoring, and validation processes to ensure reliability and trust in analytical outputs.
• Optimize data workflows for performance, cost-efficiency, and maintainability across cloud and on-prem environments.
• Translate complex data narratives into actionable business insights.
• Work closely with business stakeholders to understand requirements and deliver data-driven solutions.
• Architect analytical systems that support business strategy, objectives, and values.
• Contribute to use case roadmaps and prioritization aligned with strategic goals.
• Stay abreast of developments in ML, analytics engineering, and data infrastructure to drive innovation.
• Mentor junior data scientists and contribute to quality assurance across the team.
• Collaborate cross-functionally with data science, engineering, BI, and business teams.
• Promote ethical AI practices and ensure models in production are aligned with responsible AI principles.
• Support Nedbank’s culture-building and corporate responsibility initiatives.

Essential Qualifications - NQF Level

• Matric / Grade 12 / National Senior Certificate
• Advanced Diplomas/National 1st Degrees

Preferred Qualification

• BSC Computer Science, Engineering, Econometrics, Mathematical Statistics, Actuary Science or any STEM qualification
• Masters Degree Computer Science, Engineering, Econometrics, Mathematical Statistics, Actuary Science or any STEM qualification. 

Preferred Certifications

• Machine Learning and Data Engineering related

Minimum Experience Level

• 7 years’ plus  experience in a statistical and/or data science role.

Type of Exposure

• Data Science
• ML Engineering
• Data Warehousing
• Advanced analytics
• Marketing analytics
• Financial analytics
• Presentations skills 
• Predictive analytics
• Data mining
• Strategy formulations 

Technical / Professional Knowledge

• Strong proficiency in Python (required), with experience in R, Scala, or SQL.
• Experience with distributed computing tools (e.g., Spark, Ray) and cloud platforms.
• Familiarity with graph databases (e.g., Neo4j, TigerGraph) and graph analytics.
• Deep understanding of the data science lifecycle and analytics engineering principles.
• Experience in the financial services domain, with knowledge of regulatory and business-specific data contexts.
• Excellent communication skills and ability to work in cross-functional teams.
• Exposure to feature store platforms, ML model deployment, and MLOps practices.

Behavioural Competencies

• Strong problem-solving skills
• Good communication skills
• Ability to work in teams
• Decision Making
• Innovation
• Continuous Improvement