Role Description We are looking for an Ontology Architect to lead high-impact applied Engineering and analytics initiatives. This role combines deep technical expertise, strong experimentation rigor, and business leadership to influence product direction and drive measurable outcomes at scale. - Ontology & Taxonomy Design: Lead the creation, modeling, and evolution of enterprise-wide ontologies, taxonomies, and controlled vocabularies that accurately represent complex business domains. - Knowledge Graph Architecture: Design and implement scalable architecture, ingestion pipelines, and governance for enterprise Knowledge Graphs (Triple Stores or Property Graphs). - Semantic Layer Strategy: Build and maintain the enterprise semantic layer to abstract physical data complexities, providing a unified, machine-readable business view of data. - Data Product Augmentation: Partner with domain data teams to map, link, and augment decentralized Data Products using the central ontology to ensure semantic interoperability across the organization. - Inference & Reasoning: Implement semantic reasoning and inference rules to automatically generate new metadata and uncover hidden insights within the graph. - Governance & Standards: Establish best practices, version control mechanisms, and data contracts for semantic models, ensuring consistent graph schema updates across business units. - Semantic Standards: Expert-level mastery of core semantic technologies. - Knowledge Graph Engineering: Hands-on experience designing and operating production-grade Graph Databases / Triple Stores (e.g., GraphDB, Stardog, Amazon Neptune, AllegroGraph, or Neo4j). - Ontology Modeling Tools: Proficiency with industry-standard ontology engineering and taxonomy management software (e.g., Protégé, TopBraid Composer, PoolParty). - Modern Data Frameworks: Clear, practical understanding of Data Mesh paradigms, specifically how to design a semantic layer that overlays federated, domain-driven Data Products. - Traditional Data Modeling: Strong baseline in classic data concepts, including relational databases, dimensional modeling, and ETL/ELT integration patterns. Qualifications - Hands-on experience to enforce data quality and constraint validation across graph structures. - Familiarity with graph algorithms, graph machine learning (GNNs), or leveraging Knowledge Graphs to enhance Large Language Model architectures via Graph RAG (Retrieval-Aug Generation). Benefits - Significant career development opportunities exist as the company grows. - The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility. Company Description Tiger Analytics is an advanced analytics consulting firm. We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data. Our consultants bring deep expertise in Data Science, Machine Learning, and AI. Our business value and leadership have been recognized by various market research firms, including Forrester and Gartner.

• Generate ad creatives, graphics, short-form video, and copy using AI tools (Midjourney, DALL-E, Canva AI, Runway, ChatGPT/Claude). • Build and launch conversion-focused websites and landing pages using AI-assisted, no-code/low-code builders (Webflow, Framer, Wix, WordPress). • Plan and run social media (LinkedIn, Facebook/Instagram), using AI to scale ideation, drafting, and scheduling. • Set up and manage Google Ads and Meta Ads accounts; build campaigns, audiences, and creative tests. • Help build and launch cold email and outbound campaigns: list setup, sequencing, copywriting, and deliverability basics (Instanly, Clay, Smartlead a plus). • Own go-to-market execution: brand assets, messaging, funnels, and the systems to capture and convert demand.

Role Description We are seeking a PhD intern to join us for a Fall 2026 engagement (targeting a September 2026 start, with flexibility). This is a hands-on applied research role: you will be expected to design and run experiments, build prototypes, and contribute meaningfully to ongoing team projects in one or more of our core domain areas. Domain Areas - Agentic AI – Developing and evaluating systems that reason, plan, and act autonomously, including tool use, retrieval-augmented reasoning, memory and goal management, and feedback-driven learning. - LLM Evaluations – Designing rigorous evaluation frameworks, benchmarks, and quality metrics to assess language model behavior, reliability, and alignment. - Multimodal Data – Building models and pipelines that integrate text, image, video, audio, and other data modalities; experience with large vision-language models and modality fusion. - LLM Training Data Curation – Researching and implementing methods for selecting, filtering, and improving training data quality to enhance model performance. Qualifications - Currently enrolled PhD student in Computer Science, Machine Learning, Artificial Intelligence, Computer Engineering, Mathematics, Statistics, Data Science, Cognitive Science, or a related field - Able to work 40 hours per week during the fall/winter - Proficiency in Python - Strong foundation in machine learning, deep learning, and algorithms/statistics - Experience with one or more major ML frameworks: PyTorch, TensorFlow, or JAX - Ability to design, run, and interpret ML experiments in an applied research setting - Ability to translate research ideas into practical prototypes and evaluations - Strong oral and written communication skills for presenting technical work clearly Requirements - Coursework or research experience in advanced NLP, advanced ML systems, or reinforcement learning - Familiarity with HuggingFace, Transformers, Pandas, NumPy, and scikit-learn - Publications in top venues such as NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, AAAI, CIKM, WWW, UAI, CVPR, or related - Experience with agentic systems, multimodal modeling, or applied LLM workflows - Exposure to evaluation design, benchmarking, and model quality tradeoffs - Familiarity with distributed computing environments such as Spark or Presto - Comfortable with software engineering best practices (version control, testing, code review) Benefits - Comprehensive health plans - Mental health support - 401(k) Retirement Plan with employer match - Stock Option Program - Disability Programs - Health Savings and Flexible Spending Accounts - Family-forming benefits - Life and Serious Injury Benefits - Paid leave of absence programs - Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off - Full-time salaried employees are immediately entitled to flexible time off

• Identify, prioritize, and develop AI use cases that improve business performance and operational efficiency. • Lead AI proof-of-concepts, pilots, and adoption initiatives. • Analyze business processes and design future-state workflows leveraging AI, automation, and analytics. • Gather and document business requirements, process flows, user stories, and functional specifications. • Support global programs and operational initiatives across multiple business functions. • Develop operational playbooks, governance models, and standard operating procedures. • Design AI Enabled dashboards, scorecards, and executive reporting solutions, chatbot, actionable insights. • Leverage AI tools to generate playbooks, training materials, and knowledge assets, partner profitability modeling, sales plays and actionable insights. • Partner with IT, business stakeholders, and vendors to evaluate and implement technology solutions. • Conduct training AI knowledge with team members