Data Scientist (UAE)

1. Generative AI & NLP for Engineering * Data Exploration and Analysis: Query and analyse large domain- or topic-specific data sets from both structured and unstructured sources, identify patterns and features. Ensure data meets quality standards and requirements before model development. * Regulation Text Interpretation: Design and fine-tune Large Language Models (LLMs) to parse complex regulatory texts (e.g., building codes, military standards) and extract structured rules for automated compliance checking. * Rule Formalization: Convert interpreted regulations into computer-processable formats (e.g., object-property-condition-value tuples) that can be executed by downstream compliance engines. * Querying via NLP: Architect methods for LLMs to map natural language requirements directly to specific metadata entities within various schemas (e.g., mapping “systems design” to specified attributes). * RAG Architecture: Implement Retrieval-Augmented Generation (RAG) pipelines that allow systems to query vast repositories of technical documentation and historical project data with high accuracy and low hallucination rates.

2. Predictive Modeling & Optimization (Supply Chain) * Forecasting Engines: Develop time-series forecasting models to predict spend categories and material demand by correlating internal ERP data with external macroeconomic signals. * Classification & Risk Scoring: Build machine learning classifiers to categorize supplier risks and operational anomalies, integrating data from diverse sources to create dynamic risk scores. * Data Extraction Pipelines: Design robust pipelines to extract and transform raw data (from Data Lakehouse, external web sources, or SAP and other databases) into features required for predictive modeling and automated rule checking.

3. System Integration & Performance * Model Orchestration: Work with Back End Engineers to integrate AI models into a cohesive “compliance engine” or “risk engine” that can be invoked programmatically via robust APIs. * Optimization: Streamline model performance to ensure complex checks (e.g., analyzing large datasets or processing thousands of supplier records) can be executed within reasonable timeframes, potentially using batching or asynchronous processing. * Quality Assurance: Validate model outputs against known test cases and historical data, debugging false positives/negatives to refine algorithms and ensure “defense-grade” reliability., 43 Minutes Ago Remote or Hybrid Texas, USA 16-16 Hourly Junior 16-16 Hourly Junior Fintech * Professional Services * Sales * Financial Services Provide empathetic phone and email support to members, assist with onboarding and account setup, handle high-volume inbound calls, document interactions, and collaborate with team members to share best practices. Achieve

Customer Service

43 Minutes Ago Remote or Hybrid 18-18 Hourly Junior 18-18 Hourly Junior Fintech * Professional Services * Sales * Financial Services Provide empathetic phone and email support to members for onboarding, account setup, and ongoing inquiries. Listen, troubleshoot, document interactions, collaborate with team, and maintain high call volumes while coaching members toward financial solutions. Navan

Event Travel Manager

50 Minutes Ago Easy Apply Remote or Hybrid USA Easy Apply 68K-70K Annually Junior 68K-70K Annually Junior Fintech * Information Technology * Payments * Productivity * Software * Travel * Automation Serve as primary client liaison for Event Travel, providing end-to-end travel support, fare and booking management, program coordination from post-sale to billing, troubleshooting using agent tools, maintaining documentation and SLAs, training on travel platforms, and supporting team leads to maximize revenue and client ROI. Top Skills: AmadeusGoogle WorkspaceExcelMicrosoft PowerpointMicrosoft WordNavan TravelxenNavan/R&M SystemsNdcSabreSlack

What you need to know about the Colorado Tech Scene

With a business-friendly climate and research universities like CU Boulder and Colorado State, Colorado has made a name for itself as a startup ecosystem. The state boasts a skilled workforce and high quality of life thanks to its affordable housing, vibrant cultural scene and unparalleled opportunities for outdoor recreation. Colorado is also home to the National Renewable Energy Laboratory, helping cement its status as a hub for renewable energy innovation.

Key Facts About Colorado Tech

• Number of Tech Workers: 260,000; 8.5% of overall workforce (2024 CompTIA survey)
• Major Tech Employers: Lockheed Martin, Century Link, Comcast, BAE Systems, Level 3
• Key Industries: Software, artificial intelligence, aerospace, e-commerce, fintech, healthtech
• Funding Landscape: $4.9 billion in VC funding in 2024 (Pitchbook)
• Notable Investors: Access Venture Partners, Ridgeline Ventures, Techstars, Blackhorn Ventures
• Research Centers and Universities: Colorado School of Mines, University of Colorado Boulder, University of Denver, Colorado State University, Mesa Laboratory, Space Science Institute, National Center for Atmospheric Research, National Renewable Energy Laboratory, Gottlieb Institute

• Core AI/ML: Expert proficiency in Python and standard ML libraries (TensorFlow/PyTorch, Scikit-learn, Pandas, NumPy). Strong grasp of both supervised and unsupervised learning techniques.
• NLP & LLMs: Deep experience with transformer-based models (GPT, BERT, Llama) and prompt engineering techniques (few-shot learning, fine-tuning) for domain-specific tasks.
• Data Engineering: Proficiency in handling complex data structures (JSON, XML) and familiarity with database querying (SQL/NoSQL) or graph data structures. Experience with data extraction from specialized formats is a significant plus.
• Backend Awareness: Understanding of how to expose models via RESTful APIs (Flask/FastAPI) and integrate them into larger software architectures.
• Statistics: Solid understanding of statistics, probability distribution, A/B testing. Adept at identifying and mitigating biases in datasets