Skills

Full description

Bachelors degree in Computer Science, Statistics, Applied Mathematics, or a related field.
Proficient in Python programming, with experience using data analysis libraries such as Pandas, NumPy, and SciPy.
Strong expertise in SQL and experience with relational databases.
Familiarity with Jupyter for interactive data analysis.
Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy.
Experience in creating visualizations and dashboards using tools like Matplotlib, Seaborn, or similar libraries/frameworks.
Excellent problem-solving skills and the ability to work independently or as part of a team.
Effective communication skills, with the ability to translate complex data into actionable insights for non-technical stakeholders.
Experience with data warehousing and ELT tools.
Knowledge of machine learning techniques and algorithms.
Familiarity with version control systems, preferably Git.
Note that we are a Mac/Linux shop and do not use the Microsoft stack. We use Snowflake for their data lake and Superset for dashboarding.
Nice to Have: Experience with handling data from market research surveys (SPSS files) and sampling statistics will be useful.

Familiarise yourself with the nature of data that they hold for customers and internally
Use your knowledge of the business and data entities to construct queries to gather required data using SQL and Python/pandas
Package the required data in user-friendly spreadsheet and graphical outputs
Take the lead on automating and delivering regular reporting required by customers and business
Collaborate with internal teams to understand their data analysis needs and develop reports and dashboards that provide clear insights into key business metrics.
Work with our data engineer to specify views required to streamline your work.
Apply statistical methods and data modelling techniques to identify trends, patterns, and insights
Create and maintain documentation for data analysis processes and decision-making frameworks.

Data Analyst

Compensation

Not disclosed

Location

Sandton, GP, South Africa

Work setup

Not specified

Seniority

Not specified

Employment

Not specified

Experience

Not specified

Bachelors degree in Computer Science, Statistics, Applied Mathematics, or a related field.
Proficient in Python programming, with experience using data analysis libraries such as Pandas, NumPy, and SciPy.
Strong expertise in SQL and experience with relational databases.
Familiarity with Jupyter for interactive data analysis.
Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy.
Experience in creating visualizations and dashboards using tools like Matplotlib, Seaborn, or similar libraries/frameworks.
Excellent problem-solving skills and the ability to work independently or as part of a team.
Effective communication skills, with the ability to translate complex data into actionable insights for non-technical stakeholders.
Experience with data warehousing and ELT tools.
Knowledge of machine learning techniques and algorithms.
Familiarity with version control systems, preferably Git.
Note that we are a Mac/Linux shop and do not use the Microsoft stack. We use Snowflake for their data lake and Superset for dashboarding.
Nice to Have: Experience with handling data from market research surveys (SPSS files) and sampling statistics will be useful.

Familiarise yourself with the nature of data that they hold for customers and internally
Use your knowledge of the business and data entities to construct queries to gather required data using SQL and Python/pandas
Package the required data in user-friendly spreadsheet and graphical outputs
Take the lead on automating and delivering regular reporting required by customers and business
Collaborate with internal teams to understand their data analysis needs and develop reports and dashboards that provide clear insights into key business metrics.
Work with our data engineer to specify views required to streamline your work.
Apply statistical methods and data modelling techniques to identify trends, patterns, and insights
Create and maintain documentation for data analysis processes and decision-making frameworks.