Purpose of the Role:

At Dis-Chem Life, we are building more than just another insurance company, we are building trust, REAL protection, and peace of mind for everyday South Africans just like you. Our mission is to make life insurance smarter, fairer, and more human. To achieve this, we need people who see data not just as numbers, but as stories that guide decisions and change lives.

Our Junior Data Scientist role exists because every insight matters. You will help turn raw, messy data into solutions that improve our products, speed up claims, and enhance the customer experience. You will work closely with senior data scientists and cross-functional teams to ensure the decisions we make are grounded in data, while learning and growing in a fast-paced, high-impact environment.

This role is about ownership, curiosity, and grit. You will roll up your sleeves, experiment, learn, and contribute to real business outcomes. Every line of code, every model, and every insight you generate will have tangible impact, directly power change and shaping the future of insurance.

Role Summary:

As a Junior Data Scientist at Dis-Chem Life, you will support data-driven initiatives across the business by preparing, cleaning, and exploring datasets, contributing to early-stage predictive modelling, and creating reports and visualizations to communicate insights effectively. You will partner with the Senior Data Scientist, collaborating closely to implement analyses, build models, and deliver actionable outputs.

Using tools like Python, R, SQL, and visualization platforms, you will apply statistical techniques, basic machine learning methods, and analytical reasoning to scoped business problems. Success in this role requires curiosity, attention to detail, and a hands-on approach to working with complex or imperfect data, while continuously learning and building your technical skills. Through your contributions, you will help deliver projects that inform decisions, improve processes, and enhance the customer experience.

Benefits:

• Competitive Salary
• Opportunity to learn directly from seasoned Data Scientists and Actuarial teams through structured mentorship and real-world projects.
• Be part of a company committed to nurturing talent and offering career advancement opportunities.
• Access to training, and modern technologies that accelerate your development.
• A collaborative, inclusive, and growth-focused environment that values curiosity, innovation, and continuous learning.
• The chance to influence the future of life insurance in South Africa and contribute to high-impact projects.
• Flexible working hours with hybrid options.
• Visionary Leadership

Key Responsibilities:

• Prepare, clean, and transform raw datasets (structured and unstructured) into analysis-ready formats.
• Conduct exploratory data analysis (EDA) and statistical modelling to identify patterns, anomalies, and actionable insights.
• Support the design, testing, and deployment of predictive and advanced analytics models under the guidance of senior data scientists.
• Assist with hypothesis-driven research, experiment design, and data validation to support evidence-based decision-making.
• Develop clear data visualizations, reports, and dashboards to communicate insights effectively to technical and business stakeholders.
• Collaborate with the Senior Data Scientist and cross-functional teams (marketing, claims, operations, finance) to solve business challenges with data.
• Monitor and validate models for accuracy, fairness, and reliability.
• Document workflows, ensure reproducibility, and apply best practices in version control.
• Provide research support for business projects and ad-hoc analytical requests.

Soft Skills:

• Demonstrates a natural drive to ask questions, test hypotheses, and explore data.
• A sharp eye for detail in data accuracy, ensuring reports and dashboards are reliable and insightful.
• Maintains precision in modelling, coding, and statistical validation.
• Strong communication skills, with the ability to present data-driven insights to both technical and non-technical teams in a clear and actionable way.
• Comfortable navigating ambiguity, messy real-world data, and evolving priorities.
• Shows persistence in debugging, experimenting, and refining models.
• Managing multiple tasks and projects in a dynamic environment, while meeting deadlines and delivering quality work.
• Takes ownership of their work, is self-disciplined, and proactively generates solutions.
• Continuously seeks to learn new analytical techniques and tools.
• Is eager to learn, develop, and stretch beyond their current comfort zone.

Technical Skills:

• Strong practical experience in Python, including libraries such as Pandas, NumPy, Scikit-learn, and Matplotlib, Seaborn, or Plotly.
• Confident in SQL, able to query, join, and aggregate data across relational databases to extract insights.
• Applies solid statistical knowledge, including regression, classification, probability, distributions, and hypothesis testing.
• Exposure to machine learning foundations, including supervised and unsupervised learning techniques such as regression, clustering, and decision trees.
• Can create data visualizations and dashboards using Power BI, Tableau, or Python visualization libraries to clearly communicate insights.
• Works effectively with version control systems such as Git/GitHub to collaborate, track changes, and ensure reproducibility.
• Exposure to cloud platforms such as AWS, GCP, or Azure, or experience working with big data environments. (Advantageous)

Experience:

• 2-3 years of experience in data science, analytics, or applied research, including internships, academic projects, or industry roles.
• Hands-on experience with Python and SQL for data cleaning, transformation, and analysis.
• Exposure to statistical modelling and predictive analytics applied to practical business problems.
• Collaborative experience working with senior data scientists or cross-functional teams on analytical projects.
• Experience creating visualizations, reports, or dashboards to communicate insights clearly.
• Research experience in statistics, advanced analytics, or machine learning. (Advantageous)
• Experience in insurance or financial services, applying analytics to business decisions. (Advantageous)

Qualifications:

• Bachelor’s degree in data science, Statistics, Computer Science, Mathematics, Actuarial Science, or a related quantitative field.
• Postgraduate study, research background, or relevant certifications (e.g., Python, ML, cloud) (Advantageous)