Senior Data Scientist

Job Title: Senior Data
Scientist

Experience: 5 – 7 years

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

• Strong
  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)