AI & Data Science Lead

X, bigly labs

. Act as custodian of the AI and Data Science craft across Data Science, Machine Learning, AI Engineering and MLOps/LLMOps .

Posted 11/6/2026full-timeJohannesburg • South AfricaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in AI and Data Science, with a strong focus on leading technical teams, establishing standards, and delivering production-ready solutions. Proficient in predictive modeling, generative AI, and MLOps practices, ensuring measurable business outcomes and effective communication with stakeholders.

Highest-signal resume keywords

AI And Data Science LeadershipPredictive Modeling And ExperimentationGenerative AI And LLM ApplicationsMLOps/LLMOps ExpertiseTechnical Standards And Communities Of Practice

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ScienceMachine LearningAI EngineeringPredictive ModelingStatistical LearningOptimizationExperimentationArchitecture DecisionsDeploymentMonitoring

Soft Skills

Technical LeadershipTeam ManagementCommunicationInfluencing StakeholdersMentoring

Tools & Technologies

DatabricksAWSMLflowUnity CatalogFeature StoreModel ServingMosaic AI

Certifications & Qualifications

Bachelor’s Degree In Computer SciencePostgraduate Qualification In Data Science

Industry Keywords

Generative AIRAGEnterprise AI GovernanceModel Risk ManagementHealthcareRetailDemand ForecastingPricing

Tech Stack

Tools & technologies

AWSCloudUnity

About the role

Key responsibilities & impact

Act as custodian of the AI and Data Science craft across Data Science, Machine Learning, AI Engineering and MLOps/LLMOps
Lead day-to-day technical direction, craft operations and delivery assurance across tribes and domains
Translate enterprise AI and Data Science strategy into technical roadmaps, architecture decisions, production-ready solutions and measurable business outcomes
Provide expert oversight of predictive modelling, forecasting, optimisation, experimentation, generative AI, RAG, agents and enterprise integrations
Establish technical standards, quality gates, reusable capabilities and Centres of Excellence
Develop technical talent, role profiles, competency frameworks and technical expectations
Define standards for data science, AI engineering, ML/LLM lifecycle, experimentation, documentation, testing, version control and deployment
Set quality gates for data suitability, model validation, bias and robustness checks, LLM evaluation, security, deployment readiness and post-release monitoring
Establish reproducible development and deployment practices, model and experiment tracking, registries, release controls and monitoring with platform teams
Establish and run AI and Data Science communities of practice with charters, roadmaps and knowledge-sharing
Lead architecture and engineering practices for LLM applications, RAG, AI agents, tool use, APIs, orchestration and enterprise system integration
Communicate complex technical choices to executives and non-technical stakeholders and influence delivery leaders, governance partners and technical teams

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering or a related quantitative field
10+ years of relevant experience across data science, machine learning and/or AI engineering, including delivery of production solutions
5+ years leading technical professionals, with experience managing teams and influencing delivery in a matrix organisation
Proven hands-on technical leadership in advanced data science or AI engineering, with breadth across predictive ML, GenAI and production AI systems
Accountability for end-to-end AI/ML delivery, including problem framing, modelling, experimentation, architecture, deployment, monitoring, reliability and measurable business outcomes
Experience with generative AI, LLM applications, RAG, evaluation, guardrails, prompting, retrieval and fine-tuning
Ability to establish technical standards, reusable patterns, communities of practice or Centres of Excellence
Experience recruiting, coaching, performance-managing and developing technical talent, including contractors and consultants
Ability to make and defend complex architecture and engineering decisions, lead senior technical discussions and translate strategy into execution
Experience with advanced predictive modelling, forecasting, optimisation, experimental design and statistical learning
Experience leading technical roadmaps, production readiness reviews and cross-functional delivery of enterprise AI solutions, including cost, risk, scalability and build-versus-buy trade-offs
Strong knowledge of MLOps/LLMOps, cloud platforms and software engineering
Ability to challenge architecture, code, experiments and model performance constructively
Experienced leader and mentor in matrix teams
Ability to communicate complex technical choices to executives and non-technical stakeholders
Advantageous: postgraduate qualification in Data Science, AI, Computer Science or a related field
Advantageous: experience in retail, pharmacy, healthcare, loyalty, demand forecasting and pricing
Advantageous: hands-on Databricks on AWS experience, including MLflow, Unity Catalog, Feature Store, Model Serving and Mosaic AI
Advantageous: experience with model risk management, privacy, responsible AI and enterprise AI governance

Benefits

Comp & perks

No specific benefits, perks, or compensation extras are stated in the posting