Role Overview

We are looking for a strong, hands -on,
development -oriented Data Scientist with solid experience in Python,
Machine Learning, and statistical analysis. The ideal candidate should be
capable of translating complex business problems into scalable, data -driven
solutions and working with large, real -world datasets.

Key Responsibilities

- Develop,
validate, and deploy machine learning models for real -world business use
cases

- Perform
data extraction, transformation, and analysis using SQL and Python

- Apply
statistical methods and machine learning techniques such as regression,
classification, clustering, and time -series forecasting

- Conduct
data cleaning, feature engineering, model evaluation, and performance
tuning

- Build
and maintain end -to -end data pipelines and ETL workflows

- Visualize
insights and model outcomes using dashboards and reports

- Collaborate
with business stakeholders to understand requirements and deliver
actionable insights

- Ensure
compliance with data privacy and governance standards


Required Skills & Qualifications

- Solid
proficiency in Python (Pandas, NumPy, Scikit -learn)





- Excellent SQL skills for data extraction and analysis

- Hands -on
experience with Machine Learning algorithms (regression,
classification, clustering)

- Solid
understanding of statistics and probability concepts

- Experience
with time series analysis and forecasting

- Strong
experience in data cleaning, feature engineering, and model evaluation

- Proven
ability to work on large, complex datasets


Tools & Technologies

- ML
Libraries: Scikit -learn; TensorFlow / PyTorch (preferred)

- Data
Visualization: Power BI, Tableau, Matplotlib, Seaborn

- Big
Data (Good to have): Spark / PySpark

- Version
Control: Git / GitHub

- Cloud
Platforms: AWS, Azure, or GCP

- Data
Platforms: Snowflake, BigQuery, Redshift (preferred)

- Understanding
of ETL frameworks and data pipelines


Business & Domain Exposure

- Strong
ability to translate business problems into analytical solutions

- Experience
working closely with stakeholders and cross -functional teams

- Domain
exposure to Banking, Insurance, Retail, or Telecom is a plus

- Experience
in risk modeling, customer analytics, or fraud detection is
desirable

- Awareness
of data privacy and compliance standards (POPIA knowledge is an
added advantage)