Jobs at Tiger Analytics

Leads AI and Generative AI engagements from discovery through production, serving as the client and technical delivery lead. Combines project management with hands-on architecture and development of LLM applications, RAG pipelines, AI agents, APIs, and cloud-based services. Coordinates cross-functional teams, manages risks and delivery milestones, translates business needs into technical solutions, and oversees evaluation, monitoring, deployment, and production readiness.

Leads client-facing AI and Generative AI engagements from discovery through production while combining project leadership with hands-on engineering. Responsibilities include architecting and deploying LLM applications, RAG systems, AI agents, and production AI services; coordinating technical teams; managing delivery risks and timelines; and translating business needs into practical AI solutions. The role requires expertise in Python, cloud platforms, LLM frameworks, vector databases, APIs, microservices, deployment, evaluation, monitoring, and responsible AI.

Business-first consultant who partners with senior stakeholders to understand decisions, structure ambiguous problems, analyze data, and develop actionable recommendations. The role involves prototyping data- and AI-enabled solutions using Generative AI, Agentic AI, analytics, and low-code tools; coordinating data scientists, engineers, and domain experts; and translating insights into measurable business outcomes.

Business-first consultant who partners with leaders to structure ambiguous decisions, analyze data, prototype GenAI and agentic solutions, and translate insights into measurable business outcomes. The role requires stakeholder management, hands-on analytical work, rapid solution prototyping, executive communication, and collaboration with data science, engineering, product, and domain teams.

The Business Analyst will gather and document future-state requirements, define metadata and field mappings, document process flows and business rules, and support agile backlog and Jira story readiness. The role partners with product owners and cross-functional stakeholders across geoscience, legal, finance, engineering, and business teams to translate business needs into build-ready requirements for PIM, MDM, master data, and enterprise process initiatives.

Manage the product backlog, refine user stories, coordinate Agile sprints, support UAT and release activities, and translate stakeholder feedback into delivery priorities. The role uses Jira for workflow and sprint tracking and SQL for data validation and analysis. It collaborates with business, process, and technical teams to ensure requirements are defined, prioritized, and aligned with business needs.

Supports due diligence, portfolio monitoring, and investment analysis by collecting, validating, integrating, and governing land, mineral, lease, title, deed, and royalty data. The role gathers requirements, documents processes, develops reporting and KPIs, improves data quality, and translates business needs into actionable analytical and operational solutions. It collaborates with business, IT, vendors, geoscience, legal, finance, and engineering stakeholders while coordinating testing, implementation, and multiple priorities.

Manage analytics consulting projects from problem definition through solution delivery. Translate client business challenges into analytics designs, apply statistical and machine learning techniques, present insights to executives, and develop plans to operationalize solutions. Collaborate with global consulting, IT, and data engineering teams while managing stakeholders and project teams.

Improve and simplify New Product Setup workflows in Appian across regions and ERPs. The role designs future-state processes, rationalizes fields, strengthens governance, standardizes workflows, documents operating practices, and identifies process inefficiencies. It partners with stakeholders, process owners, and product owners to translate pain points into practical improvements, support agile requirements readiness, and ensure enhancements are reflected in delivery plans.

The Product Owner manages the New Product Setup backlog, refines user stories, coordinates sprint execution, and aligns delivery with business needs. Responsibilities include UAT planning, defect tracking, stakeholder feedback, release readiness, deployment support, and validation of requirements. The role uses Jira for backlog and sprint management and SQL for business and system-data analysis, while collaborating with business, process, development, testing, and technical teams.

Leads technology advisory, client engagement, presales, solution architecture, and practice development for enterprise data and AI transformations. Provides thought leadership, assesses client platforms, develops modernization roadmaps, creates architectures and proposals, and mentors engineering, data, and architecture teams. Requires deep hands-on Databricks and Azure expertise, experience with enterprise Lakehouse platforms, streaming and batch pipelines, governance, security, and large-scale transformation programs. Partners with senior executives and offshore teams to drive delivery quality, talent development, and consulting growth.

Lead hands-on architecture, development, deployment, and optimization of enterprise GenAI, RAG, Agentic AI, and Neo4j Knowledge Graph solutions. Build data pipelines, APIs, microservices, and cloud-native AI infrastructure; rapidly deliver customer POCs and production systems. Serve as the primary technical interface for enterprise clients, troubleshooting performance, scalability, reliability, and cost issues while providing technical consulting.

Leads complex analytics consulting programs from problem definition through production deployment. Manages executive client relationships, translates business challenges into analytics solutions, and oversees global data science and engineering teams. Guides scalable AI/ML and data architecture design, applies MedTech expertise to validate results, quantifies commercial impact, and embeds analytics into business processes. Requires extensive MedTech consulting experience, leadership, project management, and strong SQL, R, and Python skills.

Own new-logo growth and account expansion across North American upstream E&P and midstream operators. Build pipeline through an established Energy network, develop senior client relationships in Houston, position analytics and AI offerings, and lead proposals and pursuits through close. Translate data, IT/OT, IoT, predictive asset performance, and production optimization capabilities into client outcomes while representing the firm at industry events and informing practice strategy.

Lead strategic programs comprising multiple concurrent projects, aligning goals with organizational objectives. Manage program schedules, dependencies, budgets, risks, milestones, and performance metrics. Remove technical and operational blockers, coordinate cross-functional teams, escalate issues, and facilitate status meetings, executive dashboards, and program reviews. The role requires strong project and program management experience, SDLC and Agile methodology knowledge, and the ability to drive consensus across engineering teams.

Designs and deploys enterprise knowledge graph solutions, semantic models, ontologies, taxonomies, and data governance frameworks. Establishes metadata management, lineage, business glossaries, stewardship workflows, and data quality standards. Collaborates with business, AI/ML, and engineering teams to translate domains into scalable architectures and integrate graphs with data fabrics, data meshes, and AI systems. Serves as a technical authority on client deployments and converts ambiguous requirements into production-ready data architecture blueprints.

Own enterprise MLOps on the Databricks Lakehouse platform by designing and governing end-to-end machine learning production lifecycles. Build CI/CD/CT automation, centralized governance, security controls, deployment isolation, and real-time observability. Serve as the principal technical authority, collaborating with client-side operations and leading workshops, enablement, upskilling, and technical handovers for onsite engineering teams.

Lead supply chain analytics consulting engagements, offshore teams, and client discussions. Translate supply chain business challenges into scalable data and analytics solutions using Python, SQL, machine learning, AI, optimization, and operations research. Provide domain expertise across demand and supply planning, shipping, and inventory management; mentor analysts and data professionals; architect analytics frameworks; and communicate actionable insights to business stakeholders.

Designs and deploys advanced AI and machine learning solutions, including AI agents, RAG systems, analytical models, and data products on Google Cloud Platform. Responsibilities include preparing structured and unstructured data, developing AI agent tools, applying NLP and deep learning, collaborating with customers and stakeholders, generating business insights, and communicating analytical results.

Deploys, integrates, and scales enterprise generative AI solutions within customer engineering teams. Builds intelligent agents and multi-agent systems using Vertex AI, ADK, and MCP; connects AI systems to enterprise databases; develops real-time data pipelines; and operationalizes LLMs across cloud environments. The role also includes model training, tuning, evaluation, deployment, vector-based grounding, cloud infrastructure integration, stakeholder communication, and translating business goals into technical architectures.