Senior Data Scientist

Requirements

Key Responsibilities

• Design and implement advanced predictive models.
  
• Develop machine learning and statistical solutions.
  
• Analyse large and complex datasets.
  
• Build forecasting, classification, and recommendation models.
  
• Drive data-driven decision-making across business functions.
  
• Present insights and recommendations to stakeholders.
  
• Mentor junior Data Scientists and Analysts.
  
• Collaborate with Data Engineering teams to build scalable data solutions.
  
• Deploy and monitor machine learning models in production.
  

Minimum Requirements

• Bachelor's Degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or related field.
  
• Master's Degree preferred.
  
• 5+ years of Data Science experience.
  
• Proven experience delivering machine learning solutions in production environments.
  

Technical Skills

Essential

• Python
  
• SQL
  
• Machine Learning
  
• Statistics
  
• Predictive Analytics
  
• Data Modelling
  
• Data Visualisation
  
• Feature Engineering
  

Tools & Technologies

• Python
  
• Pandas
  
• NumPy
  
• Scikit-Learn
  
• TensorFlow or PyTorch
  
• SQL Server
  
• PostgreSQL
  
• Power BI
  
• Tableau
  
• Jupyter Notebooks
  

Cloud Platforms

• AWS
  
• Azure
  
• Google Cloud Platform
  

Preferred Experience

• Generative AI
  
• Natural Language Processing (NLP)
  
• MLOps
  
• Data Engineering exposure
  
• Big Data technologies
  
• Spark
  
• Databricks
  

Key Competencies

• Advanced Analytical Skills
  
• Business Acumen
  
• Problem Solving
  
• Communication Skills
  
• Leadership & Mentoring
  
• Stakeholder Management
  

Preferred Certifications

• AWS Certified Machine Learning
  
• Microsoft Azure Data Scientist Associate
  
• Google Professional Machine Learning Engineer
  
• Databricks Certifications
  

Success Measures

• Delivery of business value through analytics.
  
• Accuracy and performance of predictive models.
  
• Stakeholder satisfaction.
  
• Successful deployment of production-ready solutions.
  
• Continuous improvement of data-driven capabilities.