Jobs at Tiger Analytics

Lead end-to-end executive and leadership recruiting across business and technology functions, including talent mapping, sourcing, stakeholder partnership, hiring strategy, offer negotiation, and closure. Mentor recruiters, monitor pipelines and hiring metrics, and develop talent strategies for data science, analytics, data engineering, and machine learning roles. The position partners with senior business leaders and supports recruiting growth in a global analytics consulting environment.

Designs and optimizes marketing intelligence ecosystems using Power BI, DAX, scalable data models, automated dashboards, and integrated marketing data. Develops campaign measurement frameworks, scorecards, ROI and incrementality methodologies, and executive reporting. Translates complex analytics into data-backed recommendations for senior leadership, supporting budget allocation and cross-channel marketing strategy. Requires strong BI expertise, marketing analytics experience, and the ability to communicate insights to C-suite executives.

Leads analytics consulting engagements using data science and advanced analytics to solve complex business problems. Manages project planning, stakeholder coordination, cross-functional delivery, and analytical solution development. Translates business requirements into actionable insights, presents recommendations to senior stakeholders, supports pricing analytics initiatives, and collaborates with technology teams on cloud-based analytics and data engineering solutions.

Partners with senior leaders and business heads to align HR strategy with business goals. Leads workforce planning, talent and performance management, PIP governance, leadership coaching, HR analytics, employee relations, organizational effectiveness, retention, succession planning, compliance, engagement, and change initiatives across North America.

Lead requirements discovery, documentation, and UAT for enterprise data, automation, and analytics initiatives. Define business domains, map entities to source systems and datasets, conduct SME interviews, develop BRDs and FRDs, establish business rules and data requirements, review technical mappings, assess scope changes, and drive UAT defect resolution and sign-off. Requires healthcare payer expertise, UiPath experience, strong data analysis skills, and the ability to translate business needs into technical specifications.

Leads enterprise HR analytics and AI consulting engagements from discovery through delivery. Translates business challenges into scalable analytics solutions, manages senior client stakeholders, and guides cross-functional data science, data engineering, and business analyst teams. Oversees HR data transformation initiatives, advises on workforce metrics, supports embedding analytics into business processes, and identifies opportunities for solution pitches and account expansion.

Lead analytics consulting engagements for enterprise clients by translating business challenges into AI and analytics solutions. Define agentic AI requirements, workflows, and outcomes; oversee end-to-end delivery; collaborate with technical and global teams; manage stakeholders and projects; apply statistical and machine learning techniques; and help operationalize solutions that deliver measurable business value.

Define the vision, strategy, and roadmap for AI-powered products. Lead use-case evaluation, requirements, experimentation, development, deployment, adoption, and continuous improvement across Generative AI, LLM, RAG, and agentic solutions. Collaborate with engineering, data science, design, and business stakeholders; establish success metrics; manage AI risks; and communicate product value, progress, and strategy to leadership and clients.

Designs, develops, and maintains scalable Azure data pipelines and ETL/ELT solutions using Python, Scala, Spark, and Azure data services. Responsibilities include optimizing distributed processing, managing data quality and validation, monitoring and troubleshooting pipelines, supporting production deployments, and collaborating with architects, analysts, and cross-functional teams. The role also requires Hadoop experience, Control-M workflow orchestration, SQL performance tuning, and adherence to testing, version control, and CI/CD practices.

Develop and deploy machine learning and predictive models for banking and financial services clients. Responsibilities include feature engineering, model training and validation, credit and risk modeling, fraud detection, production-ready Python development, stakeholder engagement, MLOps collaboration, model monitoring, and communicating technical findings and business impact.

Own enterprise HR analytics and AI consulting engagements from discovery through delivery. Translate business needs into scalable analytics solutions, lead discovery workshops, manage senior client stakeholders, guide cross-functional data science and engineering teams, and support account expansion. Advise on HR metrics including headcount, attrition, turnover, span of control, and workforce productivity while embedding analytics into business processes.

Lead analytics consulting engagements by translating business objectives into analytical roadmaps, defining and governing KPIs, assessing data quality, and conducting hands-on data exploration using SQL and Python. Partner with stakeholders and Power BI developers to design metric structures, semantic models, wireframes, and dashboards that support strategic decision-making.

Lead analytics consulting engagements by translating business objectives into analytical roadmaps, defining and governing KPIs, assessing data quality, and conducting hands-on data exploration with SQL and Python. Partner with stakeholders and Power BI developers to design metric structures, semantic models, wireframes, and dashboards that support strategic decision-making.

Design, develop, and maintain scalable data pipelines using Databricks, Spark, Python, Scala, and Hadoop. Build and optimize ETL/ELT processes, manage batch workflows with Control-M, troubleshoot failures, and implement data validation and quality checks. Support production deployments, performance tuning, incident resolution, and ongoing maintenance while collaborating with architects, analysts, and cross-functional teams.

Leads financial crime compliance business analysis across AML, KYC, transaction monitoring, sanctions screening, investigations, and regulatory reporting. Facilitates discovery, documents requirements, maps banking data, translates regulations into monitoring rules, evaluates vendor platforms, and manages UAT. Partners with compliance stakeholders, architects, developers, data engineers, and QA teams to support modernization initiatives, resolve functional questions, manage scope, and ensure effective alert generation with controlled false positives.

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.

Deploys, integrates, and scales enterprise Generative AI solutions across Azure environments. The role develops LangGraph agentic workflows, fine-tunes and evaluates LLMs, reduces hallucinations, integrates retrieval systems and APIs, and collaborates with customer engineering teams. Responsibilities also include requirements documentation, process mapping, automation engineering, software architecture, and communicating business value.

Own the vision, strategy, roadmap, requirements, KPIs, and go-to-market execution for insurance-focused data products. Collaborate with engineering and data science teams on products, proofs of concept, and demos; communicate progress to executives; and ensure compliance with BFS regulatory, risk, and compliance requirements.

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.