Senior Data Scientist

NTT Data; Johannesburg; On-site; ZAR 900 000 - 1 300 000; Full time

Job summary

An innovative tech company in Johannesburg seeks a Senior Data Scientist to lead the design and deployment of advanced analytics solutions. This role involves building machine learning models, enhancing decision-making through data-driven insights, and collaborating with various stakeholders. Candidates should have a Bachelor's in a relevant field, over 8 years of data science experience, and proficiency in Python, R, and SQL. Competitive package and professional growth opportunities offered.

Qualifications

• 8+ years of experience in data science with at least 3 years in a senior role.
• Proven experience in developing and deploying machine learning models.
• Strong proficiency in Python, R, SQL, and ML libraries.

Responsibilities

• Build and implement machine learning models to improve forecasting accuracy.
• Monitor deployed models and ensure reliability through metrics.
• Translate complex findings into actionable insights for stakeholders.

Skills

Machine learning model development

Data engineering

Python

R

SQL

Stakeholder engagement

Education

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

Master's degree in related field

Tools

TensorFlow

PyTorch

MLflow

Kubeflow

Git

Job description

Job Summary

The Senior Data Scientist will lead the design, development, and deployment of advanced analytics and machine learning solutions that drive strategic decision-making and operational efficiency. This role requires a deep understanding of data science, data engineering, and AI concepts, and will play a pivotal role in embedding intelligent automation and predictive modelling across the organisation.

Responsibilities

• Build and implement machine learning models using structured and unstructured data to improve forecasting accuracy and enable proactive decision-making.
• Optimise model performance and scalability through hyperparameter tuning and algorithm selection to enhance efficiency and reduce computational costs.
• Implement reproducible research practices by using version control, documentation, and testing to maintain model integrity and facilitate collaboration.
• Monitor deployed models in production using performance metrics and alerting systems to ensure reliability and timely intervention.
• Automate repetitive data science tasks through scripting and workflow orchestration to increase productivity and reduce manual errors.
• Maintain high data quality standards by conducting regular audits and validation checks to support trustworthy analytics.
• Translate complex analytical findings into clear, actionable insights for non-technical stakeholders to drive informed business strategies.
• Present data-driven recommendations using compelling visualisations and storytelling techniques to influence executive decision-making.
• Collaborate with stakeholders to define key metrics and success criteria to align analytics efforts with business goals.
• Identify and implement novel AI use cases through research and experimentation to enhance business capabilities and competitive advantage.
• Implement responsible AI practices and adhere to data governance policies to maintain trust and regulatory compliance.

Qualifications

• Matric and a Bachelors degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
• 8+ years of experience in data science, with at least 3 years in a senior or lead role.
• Proven experience in developing and deploying machine learning models in production environments.
• Strong proficiency in Python, R, SQL, and ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Solid understanding of data engineering principles and cloud data architectures (e.g., Azure, AWS, GCP).
• Experience with MLOps tools (e.g., MLflow, Kubeflow, Airflow).
• Excellent communication and stakeholder engagement skills.

Advantageous

• Masters degree in Data Science, Computer Science, Statistics, Mathematics, or related field.
• Experience with large language models (LLMs) and generative AI.
• Experience in healthcare, retail, or insurance data ecosystems

Technical Skills

Machine Learning

• Expert in designing, developing, and deploying advanced machine learning and AI models.
• Expert in selecting appropriate algorithms, optimising model performance, and mentoring junior team members in best practices.

Data Engineering & Architecture

• Understanding of ETL/ELT processes and data pipeline design.
• Ability to collaborate with data engineers to ensure data quality and accessibility.

Programming & Tooling

• Advanced proficiency in Python, R and SQL
• Use of Jupyter, VS Code, Git, and other development tools.
• Contribute to code reviews and promotes clean, maintainable code practices

Cloud-Native ML Tools & Platforms

• Proficiency in deploying models using platforms like AWS SageMaker, Azure ML, or Google Cloud AI Platform.
• Familiarity with containerisation (Docker) and orchestration (Kubernetes) for scalable ML solutions.

Data Visualisation and Storytelling

• Effectively communication of complex analytical insights through compelling visualisations and narratives