Data Scientist

NTT Data; Johannesburg; On-site; ZAR 600 000 - 900 000; Full time

Job summary

A leading data services provider in Johannesburg is looking for a skilled Data Scientist to leverage advanced analytics, machine learning, and statistical modeling to drive strategic decision-making. The ideal candidate will possess strong experience in data analysis and predictive modeling, focusing on extracting actionable insights from complex datasets, ensuring data integrity and leading innovation within the organization. This role emphasizes collaboration and developing scalable data solutions for improved operational efficiency.

Qualifications

• Matric and a Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics or a related field.
• 3-5 years of experience in data science, analytics, or a related field.
• Proven experience with machine learning, predictive modelling, and statistical analysis.
• Strong proficiency in Python, R, SQL, and data visualisation tools.
• Experience with cloud platforms and big data technologies is advantageous.

Responsibilities

• Develop and maintain predictive and prescriptive models using machine learning.
• Analyse large datasets to uncover patterns and trends.
• Monitor model performance and retrain as necessary.
• Translate business challenges into analytical problems.

Skills

Machine learning

Predictive modelling

Statistical analysis

Python

R

SQL

Data visualisation tools

Cloud platforms (AWS, Azure, GCP)

Big data technologies (Spark, Hadoop)

Deep learning frameworks (TensorFlow, PyTorch)

Education

Bachelor's degree in Data Science, Computer Science, Statistics, or Mathematics

Master's degree in a related field (advantageous)

Tools

Power BI

Tableau

Git

Docker

Kubernetes

Job description

Overview

Summary of role

The Data Scientist is responsible for leveraging advanced analytics, machine learning, and statistical modelling to extract actionable insights from complex datasets. This role supports strategic decision-making, drives innovation, and enhances operational efficiency across the organisation.

Responsibilities

• Develop, implement, and maintain predictive and prescriptive models using machine learning algorithms to forecast business outcomes, enabling proactive decision-making and strategic planning.
• Analyse large and complex datasets using statistical techniques to uncover patterns and trends, driving data-informed insights and operational improvements.
• Monitor model performance using validation metrics and retrain models as needed to maintain accuracy, ensuring continued relevance and reliability of outputs.
• Translate business challenges into analytical problems using structured frameworks, enabling the development of targeted and effective data solutions.
• Collaborate with data engineers to build robust data pipelines and ensure data integrity.
• Maintain and optimize data storage solutions for scalability and performance.
• Identify opportunities for automation in reporting and analysis using scripting and APIs, increasing efficiency, and reducing turnaround time.
• Document methodologies, assumptions, and outcomes in a clear and reproducible format to support transparency governance, and knowledge sharing.
• Translate complex data into actionable insights that support strategic decision-making.
• Identify trends, patterns, and anomalies that inform business strategies and operational improvements.
• Develop and maintain dashboards and reports for various business units.
• Build end-to-end data science solutions, from prototype to production.
• Integrate models into business applications or platforms using APIs or other deployment methods.
• Monitor deployed models for performance drift and retrain as necessary.
• Experiment with new techniques to improve model performance and analytical capabilities fostering innovation and continuous improvement.
• Contribute to the development of best practices, standards, and frameworks within the data science team to ensure consistency and quality.

Qualifications

• Matric and a Bachelors degree in Data Science, Computer Science, Statistics, Mathematics or a related field.
• 35 years of experience in data science, analytics, or a related field.
• Proven experience with machine learning, predictive modelling, and statistical analysis.
• Strong proficiency in Python, R, SQL, and data visualisation tools (e.g., Power BI, Tableau).
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and big data technologies (e.g., Spark, Hadoop) is advantageous.
• Familiar with version control systems (e.g., Git) and collaborative development practices.
• Deep understanding of statistical methods, probability theory, linear algebra, and calculus to support model development and data interpretation.
• Advanced proficiency in Python, R, SQL, and familiarity with Java or Scala. Ability to write clean, efficient, and reusable code.
• Experience with supervised and unsupervised learning, deep learning frameworks (e.g., TensorFlow, PyTorch), and model evaluation techniques.
• Knowledge of data warehousing, ETL processes, and working with structured and unstructured data.
• Familiar with cloud platforms (AWS, Azure, GCP), containerization (Docker, Kubernetes), and scalable data solutions.
• Skilled in using tools like Power BI, Tableau

Advantageous

• Masters degree in Data Science, Computer Science, Statistics, Mathematics, or related field.
• Experience in healthcare, retail, or insurance data ecosystems