Team Lead Data Scientist

**Role** : • *Team Lead Senior Data Scientist (** Engineering / AI) • *Industry** : Next-generation mortgage intelligence platform (US) • *Location** : South Africa (Remote) • *Employment Type** : Full-time Contract • *Note:** Candidates must be based in South Africa and available to work • *08h00-17h00 US Eastern Standard Time (EST).** • *Note:** This • *client-facing** role requires experience with graph databases, • *particularly Neo4** j as well as experience with knowledge graphs, graph-based reasoning, or Graph-RAG architectures. • *Introduction:**

Our client ( • *US-based)** builds advanced AI systems that reason over complex data, documents, and knowledge graphs. Their platform combines machine learning, natural language systems, and graph-based reasoning to automate decision-making in document-intensive domains.

We are looking for a • *client-facing** • *Team Lead Senior Data Scientist** who is comfortable working deep in the stack, from modeling and experimentation to building production systems that integrate models, tools, and data pipelines.

This role is • *not focused on prompt engineering or simple API wrappers** . We are looking for engineers who understand how models work, how to evaluate them rigorously, and how to integrate them into scalable production architectures. • *About the Role:**

As a • *technical lead** , you will guide and design the development of • *machine learning and AI systems used in production** while working closely with clients, platform engineers, data engineers, and infrastructure teams.

You will translate complex technical concepts into practical solutions and ensure successful delivery of AI systems in real-world environments. • *Note:** This role is • *fully remote** but candidates must be available to work 08h00-17h00 US Eastern Standard Time (EST). • *Responsibilities:**

- Lead the design, development, and evaluation of machine learning models, including feature engineering, model selection, and statistical validation - Guide and mentor data scientists and engineers, setting technical direction and helping the team build scalable AI systems - Build production-grade Python systems for data processing, model training, and inference pipelines - Develop NLP systems for document understanding, classification, semantic search, and information extraction - Design and implement RAG architectures, embedding strategies, indexing approaches, retrieval evaluation, and grounding techniques - Develop agentic AI systems that coordinate models, tools, and multi-step reasoning workflows - Engage directly with clients and stakeholders to understand requirements, present solutions, and translate technical capabilities into business outcomes - Collaborate with platform and infrastructure teams to deploy and operate ML systems in production environments - Provide technical leadership during client engagements, helping shape AI strategies and guiding implementation decisions - Contribute to architectural decisions around AI system design and data modeling • *Required Skills and Qualifications:**

- Strong Python programming for ML and data systems - Experience designing and training ML models (not solely relying on hosted AI services) - Deep understanding of ML fundamentals (statistics, optimization, model evaluation) - Hands-on NLP experience (document extraction, classification, semantic search) - Experience designing and implementing RAG pipelines and retrieval systems - Experience building agent-based or tool-using AI systems (planner-executor, multi-agent coordination) - Experience with production data systems and real-world datasets - Experience leading technical projects or mentoring engineers / data scientists - Strong problem-solving ability and translating ambiguous problems into measurable systems • *Preferred Qualifications:**

- Experience with graph databases, • *particularly Neo4j** - Experience with knowledge graphs, graph-based reasoning, or Graph-RAG architectures - Experience deploying ML systems in Azure or AWS - Familiarity with MLOps practices (containerization, model deployment, monitoring, CI/CD) - Experience in document-heavy or compliance-oriented domains - Strong communication and presentation skills with clients or senior stakeholders - Experience leading technical discussions, workshops, or solution design sessions with customers