Jobs at Tiger Analytics

Leads financial crime compliance business analysis for AML modernization initiatives. Partners with compliance, risk, engineering, and IT teams to define requirements, map banking data, translate regulations into monitoring rules, evaluate vendor capabilities, conduct gap analysis, and manage UAT. Develops BRDs, FSDs, test plans, user stories, and acceptance criteria while supporting transaction monitoring, KYC, sanctions screening, investigations, case management, and regulatory reporting programs.

Designs enterprise-scale data and analytics architectures using Azure, Snowflake, and Databricks. Assesses legacy platforms, develops modernized end-to-end data ecosystems, and establishes data modeling, pipeline, security, governance, privacy, and compliance standards. Partners with senior clients and technology teams to define solutions, lead architecture reviews, guide distributed engineering teams, and drive modernization through automation and reusable frameworks.

Administer and support enterprise Snowflake environments, including security, access controls, performance, monitoring, troubleshooting, production releases, and platform stability. Support DataOps and DevOps processes, build CI/CD pipelines, automate administrative tasks with Python, SQL, and shell scripting, and maintain data ingestion and integration operations.

Build and deploy production-grade GenAI and Agentic AI solutions for enterprise clients. Responsibilities include designing LLM applications, RAG systems, AI agents, and multi-agent workflows; integrating APIs, databases, and business applications; deploying and scaling cloud solutions; optimizing reliability, security, performance, and cost; and partnering with technical and business stakeholders from initial problem definition through production deployment.

Build and deploy production-grade Generative AI and Agentic AI solutions for enterprise clients. Responsibilities include designing LLM applications, RAG systems, AI agents, and multi-agent workflows; integrating enterprise data and applications; deploying and scaling cloud-based systems; and optimizing reliability, security, performance, and cost. The role requires direct client engagement, strong software engineering skills, cloud expertise, and hands-on experience with LLM frameworks, agent orchestration, and production AI operations.

Develop and refactor Python optimization algorithms using object-oriented programming for supply chain, inventory allocation, replenishment, and pricing optimization. Build predictive models, apply advanced statistical and optimization techniques to large datasets, and deliver data-driven solutions for retail and CPG clients. Collaborate with business stakeholders and data science teams, communicate analytical insights, coach colleagues, and apply current data science practices to business problems.

Develop and refactor Python optimization algorithms for supply chain, inventory allocation, replenishment, and pricing optimization. Build predictive models, analyze large datasets, design experiments, monitor KPIs, and provide data-driven recommendations. Collaborate with business stakeholders and data science teams to deliver analytical solutions, while coaching colleagues and applying advanced statistical and operations research methods.

Manage enterprise BI consulting initiatives by gathering stakeholder requirements, designing data models and flows, communicating architecture, and overseeing Power BI dashboard delivery. Lead design reviews, data validation, governance, risk mitigation, compliance, issue resolution, and offshore collaboration. The role requires strong stakeholder management, enterprise reporting experience, and expertise with Power BI, with Alteryx and Tableau preferred.

Manage, design, deploy, optimize, and scale persistent and transient AWS EMR clusters for batch data processing. Automate infrastructure with CloudFormation, Terraform, and CodePipeline; administer EC2, S3, and related AWS services; monitor pipelines; troubleshoot performance issues; and maintain security, patching, access controls, and cluster configurations. Preferred experience includes Hadoop, HDFS, AWS EKS, containerized workloads, and Kerberos.

Designs, develops, and maintains scalable Power BI dashboards, semantic models, and reporting infrastructure. Creates optimized DAX measures and time-intelligence logic, transforms data with Power Query, improves report performance, and implements RLS and OLS security. Collaborates with data engineers, analysts, data scientists, AI teams, and business stakeholders to deliver actionable analytics solutions.

Lead advanced analytics initiatives for operations, including lakehouse data integration, maintenance and work-order analytics, asset reliability modeling, inventory forecasting, anomaly detection, and NLP of technician notes. Build Databricks Apps and production ML workflows using Python, PySpark, SQL, MLflow, and Databricks technologies. Partner directly with operations stakeholders to translate business knowledge into data definitions and self-service analytical tools.

Designs, builds, and maintains scalable ETL/ELT pipelines, cloud data infrastructure, storage, and batch or real-time processing workloads. Optimizes queries, PySpark jobs, pipelines, performance, and cloud costs. Partners with BI, analytics, data science, and AI teams to develop dimensional data marts supporting reporting and machine learning. Implements governance, data quality validation, lineage tracking, security controls, and automated infrastructure and code deployments using CI/CD practices.

Lead transaction monitoring optimization for a major bank by analyzing historical alerts and transactions, developing classification and logistic regression models, replacing broad rules with risk-based segmentation, and recommending precise monitoring scenarios. Serve as the primary onshore client contact, coordinate offshore analysts, ensure alignment with model risk governance, prepare validation documentation, and communicate findings to technical and business stakeholders.

Leads Agile delivery across multiple teams or complex value streams, facilitating Scrum ceremonies, coaching teams, resolving impediments, managing dependencies and risks, and aligning stakeholders. The role oversees backlog and sprint goals, delivery metrics, release and change management, governance, reporting, and continuous improvement. Experience with analytics programs, cloud platforms, and tools such as Azure DevOps, Jira, Confluence, and Miro is valuable.

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.

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.

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.