AI Data Analyst (Predictive Intelligence & Agentic Systems)

About the Client Our client is a Dubai-based developer shaping the future of real estate and hospitality through technology-led, design-driven projects. Their vision combines intelligent living, longevity and wellness, and autonomous systems within every development. Operating across smart home technology, robotics and agentic AI, they are building homes that think, learn and respond. They are assembling a high-calibre technology team to turn raw data into a truly predictive and personalised living experience.

Job Description

AI Data Analyst (Predictive Intelligence & Agentic Systems) Mandatory Keywords: Python, SQL, Data Lake, Machine Learning, Predictive Modeling, RAG, Vector Databases, Time-Series Analysis Peripheral Keywords: Spark, Flink, ETL/ELT, PyTorch, TensorFlow, Scikit-learn, DuckDB, Apache Parquet, Pandas, NumPy, Pinecone, ChromaDB, Weaviate, LangChain, LangGraph, Ollama, OpenAI API, MQTT, Home Assistant, IoT Telemetry, Reinforcement Learning, Statistics, Anomaly Detection, Data Visualization, Dashboards, Data Privacy, GDPR Role Overview We are seeking a sophisticated AI Data Analyst to serve as the architect of intelligence for our ecosystem. You will be responsible for managing our comprehensive Data Lake, transforming raw telemetry from smart home devices and robotic integrations into actionable intelligence. Your mission is to move beyond “reactive” automation. You will build the data pipelines and machine learning models that allow our Agentic Interfaces to anticipate user needs, recognise patterns of life, and provide a truly predictive, personalised experience. You will be the bridge between raw IoT data and the “brain” of our autonomous systems. Key Responsibilities 1. Data Lake Management & Engineering Architect and maintain a high-performance Data Lake to ingest, store, and process massive streams of event data from smart home devices, sensors, and robotic units. Design robust ETL/ELT pipelines to ensure data integrity, low-latency availability, and high-quality inputs for model training. Implement advanced Data Vectorization and Chunking strategies to prepare unstructured data for RAG (Retrieval-Augmented Generation) and long-term agent memory. 2. Pattern Analysis & Inference Training Develop and deploy Machine Learning models to identify behavioural patterns (e.g., user routines, occupancy trends, and environmental preferences). Conduct Inference Training to refine how our agents “reason” about user journeys, moving from simple triggers to complex anticipatory actions. Perform deep-dive Event Analysis to identify anomalies, optimise system performance, and improve the reliability of autonomous robot behaviours. 3. Predictive Intelligence & Personalisation Build the logic that powers Predictive Automation: ensuring the home doesn’t just react to a command, but anticipates the user’s next move. Develop “User Journey” models that allow our Agentic Interfaces to provide proactive assistance (e.g., preparing a meeting room before the user arrives or adjusting home climate based on predicted activity). Collaborate with the Robotics and AI Engineering teams to feed personalised “profiles” into the agentic workflows. 4. Analytics & Visualisation Create internal dashboards to monitor the “health” of our intelligence, tracking model accuracy, prediction success rates, and data flow bottlenecks. Translate complex data findings into actionable product insights for the engineering and design teams. Technical Requirements Data Engineering: SQL, Python (Expert), Spark/Flink, Data Lake Architecture Machine Learning: Scikit-learn, PyTorch/TensorFlow, Inference Optimisation, Reinforcement Learning (basics) Vector Databases: Pinecone, ChromaDB, or Weaviate (experience with Vectorization & Chunking) Data Processing: DuckDB, Apache Parquet, Pandas, NumPy AI Frameworks: LangChain/LangGraph (for RAG data prep), OpenAI API, Ollama IoT & Telemetry: MQTT, Home Assistant Data Exports, Time-Series Analysis Mathematics: Probability, Statistics, Linear Algebra (for model training) Preferred Qualifications Data Science Background: A degree in Data Science, Computer Science, or a related quantitative field. Predictive Modeling Experience: Proven experience building models that predict human behaviour or time-series events. RAG Expertise: Deep understanding of how to chunk and vectorise data for Large Language Models (LLMs). Agentic Mindset: An understanding of how “Agents” differ from standard “Automations”, focusing on reasoning and planning rather than just If/Then logic. Why This Role? You won’t be cleaning spreadsheets; you will be building the Cognitive Layer of a modern home. You will have the opportunity to work with cutting-edge data architectures and see your models directly influence how people interact with their physical environment. If you want to turn “Smart Homes” into “Intuitive Homes,” this is your role. Additional Suggestions for the Candidate Portfolio Focus: We value candidates who can demonstrate a project involving time-series prediction, a custom RAG pipeline, or a complex data visualisation of IoT telemetry. Scalability Mindset: As the business grows, so will our data. We need someone who thinks about how to scale data ingestion from 10 homes to 10,000. Privacy First: You must have a strong understanding of data privacy and how to anonymise/secure sensitive user telemetry while still maintaining high-utility data for ML training. Compensation & Benefits Competitive Tax-Free Salary: A market-leading monthly package in AED, commensurate with experience. Performance Incentives: Rewards linked to quality and delivery outcomes, as per Company Internal Policy. Visa & Medical Insurance: Full UAE visa sponsorship and comprehensive health cover. Paid Leave: As per Company Internal Policy and UAE Labour Law. Annual Flight Ticket & End-of-Service Gratuity: As per UAE market practice and Labour Law. Modern Tooling: Modern hardware plus cloud data and machine learning tools. Learning & Career Growth: Certification and conference support, with a clear path to leadership. Autonomy & Impact: Build the intelligence layer from the ground up and shape how AI is deployed in the physical world.