Data Scientist

Mindworx Consulting And Academy; Gauteng; On-site; ZAR 600 000 - 800 000; Full time

Job summary

A consulting firm in South Africa seeks a BI Data Scientist to leverage data for decision-making in life and non-life insurance operations. Responsibilities include analyzing datasets, developing machine learning models, and collaborating with stakeholders to create BI solutions. Candidates should possess a relevant degree, extensive experience with Power BI and T-SQL, and skills in Python or R. Strong problem-solving abilities and communication skills are essential for this role.

Qualifications

• Proven experience as a data scientist, preferably in the insurance industry.
• Effective communication skills for collaboration with teams.
• Strong understanding of statistical concepts and data modeling.

Responsibilities

• Analyze complex datasets to identify trends and correlations.
• Develop, implement, and validate machine learning algorithms.
• Build and operationalize predictive models to provide insights.

Skills

Data analysis

Machine learning

Problem-solving

Data visualization

Collaboration

Education

Bachelor's degree or diploma in Informatics, Computer Science, Statistics, Mathematics, or Information Technology

Tools

Power BI

Python

Azure Data Factory

Microsoft SQL Server

T-SQL

Job description

As a BI Data Scientist, you will play a key role in leveraging data to drive insights, inform decision‑making, and enhance our life and non‑life insurance operations.

You will work closely with cross‑functional teams to develop innovative data‑driven solutions that address business challenges and improve overall performance.

Responsibilities

• Participate in the analysis, design, development, troubleshooting and support of the reporting and analytics platform.
• Analyze complex datasets to identify trends, patterns, and correlations.
• Generate and test working hypotheses and interpret results to provide actionable insights.
• Develop, implement and validate machine learning algorithms and statistical models.
• Build and operationalize predictive models to unearth hidden insights.
• Collaborate with actuaries, underwriters, and other stakeholders to integrate data science solutions into existing workflows and processes.
• Develop BI solutions using SQL, ETL scripting, business intelligence tools, database programming and reporting tools on the Microsoft BI Stack.
• Build scalable data pipelines and infrastructure for collecting, processing, and analyzing large volumes of structured and unstructured data.
• Automate recurring processes and monitor their performance.
• Prior experience developing business intelligence solutions in large or midsize companies.
• Ability to manage multiple tasks simultaneously and react to problems quickly.
• Extensive experience with T‑SQL.
• Capability to develop, maintain, review, and explain predictive models.
• Understanding of the financial services industry desired, especially insurance.
• Experience using data visualization tools, e.g., Power BI.
• Excellent problem‑solving skills and the ability to translate business requirements into actionable insights.
• Experience with big data technologies (e.g., Hadoop, Spark, Kafka) and cloud platforms (e.g., AWS, Azure, Google Cloud Platform).

Qualifications

• Bachelor's degree or diploma in Informatics, Computer Science, Statistics, Mathematics, or Information Technology.
• Proven experience working as a data scientist or in a similar role, preferably in the life and non‑life insurance industry.
• Proficiency in programming languages such as Python, R, or Java, as well as in data analysis and machine learning libraries (TensorFlow, PyTorch, scikit‑learn).
• At least 4 years of experience in the following: Power BI (essential), Azure Data Factory (essential), Azure Synapse Analytics (essential), Python/R, C++, C#, Java (critical), Microsoft SQL Server (critical), T‑SQL (critical).
• Effective communication skills, with the ability to collaborate with cross‑functional teams and present complex ideas clearly and concisely.
• Strong understanding of statistical concepts, data‑modelling techniques, and experimental design principles.