• Build and maintain robust data pipelines processing large volumes of data • Update and optimise our data platform for speed, scalability and cost • Coordinate with different functional teams to understand and meet their data needs • Develop processes and tools to monitor and analyse model performance and data accuracy • Solve general data-related problems

• Design and configure Microsoft Fabric workspace topology across development, staging, and production environments • Define and implement OneLake structure, storage patterns, and capacity sizing • Establish Bronze landing zone standards, including: Parquet / Delta formats, Partitioning strategy, Schema-on-read patterns, Raw payload preservation • Build and manage Data Factory pipelines, including: Scheduling conventions, Dependency graphs, Backfill processes • Configure and maintain native Microsoft connectors for key business systems (e.g. Salesforce, Dataverse, and similar platforms) • Define and document per-source extraction specifications across approximately 24 operational systems • Partner with downstream analytics stakeholders to define the Bronze-to-Silver / warehouse consumption contract • Manage schema evolution, time-travel configuration, and table optimization / retention strategies • Produce as-built documentation, handoff materials, runbooks, and architecture references • Lead an audit of the existing Fabric tenant and ingestion setup during the initial phase of the engagement

• Partner with practice and delivery leadership to scale and mature our Data & AI capabilities • Define and evolve our technical vision, reference architectures, and delivery methodologies • Represent GOBI as a trusted advisor and subject matter expert in client executive conversations • Build relationships with Microsoft and technology partners to stay ahead of platform evolution • Lead solution architecture for strategic pursuits and complex proposals • Conduct executive workshops, architecture reviews, and data strategy sessions • Translate business challenges into compelling, defensible technical solutions • Develop client-ready assets: architecture blueprints, roadmaps, and business cases • Design enterprise-scale data platforms spanning ingestion, transformation, storage, analytics, and governance • Architect solutions for both real-time streaming and batch processing workloads • Define data governance frameworks leveraging Microsoft Purview and industry best practices • Ensure solutions are secure, performant, cost-optimized, and production-ready • Provide architectural governance across active engagements • Conduct design reviews, resolve technical blockers, and ensure delivery quality • Champion documentation standards, knowledge transfer, and long-term maintainability • Mentor architects and engineers, raising the bar across the team • Create high-quality technical content: blogs, white papers, reference architectures, and presentations • Represent GOBI at industry events, webinars, and Microsoft partner forums • Contribute to practice IP development and accelerator creation

Role Description Join a leading AI lab at the forefront of generative AI innovation and help shape the next generation of Large Language Models. We are seeking experienced Data Science professionals with strong expertise in statistical analysis, machine learning, predictive modeling, and quantitative reasoning to contribute to the development of advanced AI systems. In this role, you will apply your analytical expertise to create, evaluate, and refine high-quality training data that improves how AI models reason about data, statistics, and real-world analytical challenges. You will collaborate with researchers and engineers to ensure AI systems demonstrate rigorous quantitative thinking and sound data science practices. This is a full-time engagement requiring 40 hours per week during standard weekdays. Key Responsibilities - Provide Data Science Expertise - Advise research and engineering teams on statistical methodologies, experimental design, predictive modeling, and analytical best practices. - Help improve AI model performance across data science, analytics, and machine learning domains. - Design Analytical Challenges - Create complex, real-world data science tasks that test quantitative reasoning, statistical thinking, and machine learning knowledge. - Develop accurate, well-structured solutions that reflect industry best practices. - Evaluate AI-Generated Outputs - Review and assess analytical solutions generated by AI systems and subject matter experts. - Identify errors, inconsistencies, flawed assumptions, and opportunities for improvement. - Provide clear, actionable feedback to enhance model quality and reasoning capabilities. - Develop Evaluation Frameworks - Build scoring rubrics and evaluation methodologies for: - Statistical reasoning - Predictive modeling - Machine learning workflows - Data interpretation - Experimental design - Business analytics and decision-making - Ensure consistent quality standards across datasets and evaluation processes. - Collaborate Across Teams - Work closely with AI researchers, engineers, and domain experts to maintain accuracy, consistency, and rigor in training data development. Qualifications - 3+ years of professional or research experience in: - Data Science - Statistical Analysis - Machine Learning - Predictive Analytics - Applied Mathematics - Quantitative Research - Business Analytics - Strong understanding of: - Statistical inference - Hypothesis testing - Regression analysis - Machine learning algorithms - Data visualization - Experimental design - Experience working with structured and unstructured datasets. - Ability to commit to 40 hours per week during standard business days. - Excellent written communication skills with the ability to clearly explain analytical decisions and modeling approaches. Preferred Qualifications - Experience with AI systems, Large Language Models (LLMs), or agent-based workflows. - Familiarity with model evaluation, reinforcement learning from human feedback (RLHF), data annotation, or human-in-the-loop systems. - Proficiency in Python, SQL, R, or other analytical programming languages. - Experience building and deploying machine learning solutions in production environments. - Advanced degree in Data Science, Statistics, Computer Science, Mathematics, Economics, Operations Research, or a related quantitative field. Benefits - Contribute directly to the development of state-of-the-art AI systems. - Work alongside leading researchers and engineers in artificial intelligence. - Help define how future AI models reason about data, statistics, and analytical decision-making. - Apply your expertise to challenging, high-impact projects at the cutting edge of AI innovation. Engagement Details - Full-time commitment (40 hours per week). - Weekday availability required. - Opportunity to work on long-term AI research and evaluation initiatives. - Project scope and responsibilities may evolve based on research priorities and business needs.