Data Scientist Specialist

Requisition Details & Talent Acquisition Consultant

REQ 139015 - Keabetswe Modise
Closing Date: 09 May 2025

Job Family

Information Technology  

Career Stream

Application Development

Leadership Pipeline

Manage Self Professional

Job Purpose

Apply deep 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

• Specialised in the development of best-in-class statistical models and algorithms, leveraging years of experience and expertise.
• Conduct advanced statistical analysis to uncover deep insights and patterns in complex datasets.
• Provide actionable insights and strategic advice to stakeholders, drawing from an extensive background in the field of AI/ML.
• Create significant value by harnessing the potential of enterprise-wide data and translating it into valuable business solutions.
• Apply comprehensive financial services domain knowledge to analyse datasets and develop statistical models and algorithms that cater to specific financial services use cases.
• Spearheaded the integration of AI/ML solutions into existing banking systems, optimizing processes, and driving operational efficiency while adhering to industry standards.
• Experienced in deploying or contributed to deployment of multiple end to end data science solutions that has yielded significant value in the organisation at an enterprise level.
• Implement cutting-edge AI and ML solutions, establishing robust system operations and maintenance structures under the purview of senior leadership.
• Play a key role in shaping the organization's AI/ML strategy, aligning it with current and future needs.
• Lead the transformation of data science prototypes into scalable machine learning solutions ready for production deployment.
• Design dynamic ML models and systems that possess the capability to adapt and retrain as necessary, guided by years of hands-on experience.
• Periodically assess the performance of ML systems, ensuring alignment with corporate and IT strategies.
• Demonstrate an end-to-end understanding of applications and machine learning algorithms, showcasing expertise at every step.
• Pioneering the use of machine learning algorithms and libraries, setting the standard for the enterprise.
• Oversee the software engineering and design aspects of projects, providing comprehensive end-to-end solutions.
• Extensive expertise in programming toolsets (such as Python, R, etc) for data preprocessing, advanced statistical analysis, machine learning, and developing complex data pipelines is required
• Produce end-to-end designs encompassing infrastructure, security, networks, and more, collaborating with other engineering leads to ensure a comprehensive data science solution.
• Effectively communicate complex processes to non-programming experts, utilizing years of experience to simplify understanding.
• Continuously research and implement best practices to enhance existing machine learning solutions, utilizing extensive domain knowledge.

Job Responsibilities Continue

• Design customized analytics approaches for diverse problem types, capitalizing on years of experience to drive innovation.
• Utilize a deep understanding of computer science fundamentals, including data structures, algorithms, computability, complexity, and computer architecture.
• Proven track record of successfully applying big data frameworks, such as Apache Spark, to solve complex business challenges.
• Stay at the forefront of the latest tools and techniques, keeping knowledge up-to-date through continuous learning.
• Evaluate variations in data distribution that impact model performance, leveraging seasoned judgment.
• Apply advanced analytical techniques, such as machine learning (ML) and artificial intelligence (AI), to derive substantial business value.
• Extensive expertise in cloud computing and a proven track record of successfully deploying large-scale data science solutions on cloud platforms, demonstrating the ability to architect and optimize cloud-based data pipelines and machine learning models
• Collaborate with industry leaders, conduct experiments, and research best practices to provide thought leadership in the field.
• Develop and prioritize ML roadmaps, drawing from a wealth of experience to chart the course for success.
• Foster personal growth and optimize performance by mentoring and sharing knowledge with team members and stakeholders during formal and informal interactions.
• Embrace agile thinking to identify opportunities for improving business processes, models, and systems, leveraging extensive expertise to make informed decisions.
• Support the achievement of the business strategy, objectives, and values, playing a pivotal role in driving the organization's success.
• Contribute to the establishment of the Nedbank culture and participate in corporate responsibility initiatives to align with business strategy.

People Specification

• Advanced Machine Learning and AI Expertise: Demonstrated ability to develop, implement, and optimize complex machine learning models and AI algorithms.
• Strong Programming Skills: Proficiency in multiple programming languages such as Python, R, and SQL, with a deep understanding of data structures and algorithms.
• Experience with Big Data Technologies: Hands-on experience with big data tools Spark, or similar.
• Experience with Model Deployment: Proven experience in deploying machine learning models into production environments using tools like Docker, Kubernetes, or cloud services (AWS, Azure, GCP).
• Proficiency in MLOps: Knowledge of MLOps practices to streamline the deployment, monitoring, and maintenance of machine learning models.

Essential Qualifications - NQF Level

Required Qualification/s & Certification/s:

• A Master's or Ph.D. in Data Science, Computer Science, Statistics, or a related field.
• Certifications in Machine Learning, Data Analytics, or Big data Technologies are highly desirable.

Minimum Experience Level

• MS/PhD in STEM or related technical discipline
• At least 10+ combined experience in data science or ML engineering role
• Expert knowledge of machine learning, statistics, optimization, ML Engineering or related field
• Experience with R, Python, Matlab is required, programming in C, C++, Java
• Experience working with large data sets, simulation/ optimization, and distributed computing tools (Map/Reduce, Hadoop, Hive, Spark, Gurobi, Arena, etc.)
• Excellent written and verbal communication skills along with strong desire to work in cross functional teams
• Attitude to thrive in a fun, fast-paced start-up like environment

Technical / Professional Knowledge

Behavioural Competencies