Midrand: Lead Data Scientist

Key Interfaces: – Conversational Design Team – CX Development Team – CX Development Manager – CX Automation Practice Lead – 3rd Parties as appropriate, including customer technical and business representatives – Innovations Team – Presales Team Key Competencies : – Problem-Solving Skills: Excellent analytical and problem-solving skills with a keen attention to detail. – Python Proficiency: Strong knowledge of Python and standard machine learning libraries. – AWS Experience: Strong experience working with AWS, including deploying and managing machine learning solutions in the cloud. – Source Management and Repositories: Experience with version control systems (e.g., Git) and familiarity with modern software development workflows. – Full Software Lifecycle: Experience designing machine learning models from scratch and seeing them through the full software lifecycle, from concept and development to post-deployment monitoring and optimisation. – Statistical Expertise: Solid understanding of statistical concepts and techniques, with a demonstrated ability to apply them in machine learning applications. – Communication: Strong communication and interpersonal skills, with the ability to collaborate effectively in a team environment. Preferred Competencies: – Contact Centre Experience: Experience working within the contact centre industry, applying machine learning to real-world challenges. – NLP and Text Analytics: Experience with natural language processing and text analytics, particularly in domain-specific applications. – Cloud Platforms: Familiarity with cloud computing platforms, including AWS (preferred), Azure, or Google Cloud. – Data Visualization: Proficiency with data visualization tools such as Amazon QuickSight, Power BI, or Tableau. – Prompt Engineering: Experience in prompt engineering for large language models (e.g., GPT, Claude, Gemini). – LLM Frameworks: Familiarity with frameworks such as LangChain or Amazon Bedrock for deploying and integrating large language models. – Specific Python Libraries: e.g. scikit-learn, pandas, NumPy, TensorFlow, PyTorch. – Docker: Familiarity with containerization technologies like Docker, Docker Compose, and Docker Swarm is a plus. Experience using these tools to build, deploy, and scale data science applications is highly desirable.