Midrand: Lead Data Scientist posted by Selectone

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.