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.

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.

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.

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.

Looking for a Data Engineer experienced in Dataiku to design and maintain data pipelines and infrastructure, ensuring data flow and integration across teams.

The Data Engineer will architect and implement advanced analytics capabilities, manage data pipelines, and optimize data processing using AWS cloud and various analytical tools.

Build high-performance API services and production-grade Generative AI systems. Design and implement end-to-end RAG pipelines, vector stores, embeddings, and latency-optimized generators. Develop agentic AI using LangChain/CrewAI/Semantic Kernel, integrate LLM APIs and AWS Bedrock, manage CI/CD and ML pipelines, and apply evaluation frameworks to detect hallucinations and optimize performance.

Seeking an experienced Data Engineer to design and maintain data pipelines and infrastructure, collaborating with data scientists and stakeholders.

The Machine Learning Engineer will create scalable ML systems, build production data pipelines, and ensure robust deployment and monitoring of solutions.

Manage large-scale data analytics engagements, collaborate with technology teams, and present solutions to stakeholders to drive business value.

The role involves engaging with client teams to gather and analyze requirements, managing projects, acting as a point of contact, and collaborating with offshore teams to deliver analytical solutions.

Lead development and refactor optimization algorithms in Python for pricing and supply‑chain problems. Design and implement predictive models and mathematical optimization solutions, collaborate with cross‑functional teams, coach data scientists, and translate business requirements into data‑driven pricing and replenishment strategies.

Deploys, integrates, and scales enterprise generative AI solutions in customer cloud environments. Responsibilities include architecting GCP and Vertex AI infrastructure, deploying and optimizing LLMs, building RAG data and vector search pipelines, managing GPU/TPU workloads on GKE, automating infrastructure with Terraform, and advising clients on AI safety, prompt engineering, hallucinations, and inference costs. The role also collaborates with research and platform teams and requires travel to client sites.

Embed with client engineering teams to deploy, integrate, and scale enterprise generative AI solutions. Build agentic systems, train and serve LLMs with Vertex AI, integrate models with BigQuery/AlloyDB/Spanner, implement streaming inference pipelines, and ensure operational reliability while collaborating with stakeholders to translate business goals into production-grade architectures.

The GenAI Engineer will design, build, and deploy Generative AI solutions, focusing on reinforcement learning and multi-agent workflows, while collaborating with various teams to enhance model performance.