Tiger Analytics

Data Analyst

• Partner with business stakeholders to understand and translate business needs into solution requirements.
• Write efficient SQL queries to extract, manipulate, and analyze data from multiple sources.
• Use Python for data processing, automation, and analytics.
• Work with AWS /GCP/ Azure cloud services to manage and analyze data in cloud environments.
• Perform data validation, quality checks, and troubleshooting for data pipelines.
• Build reports, dashboards, and insights to support business decisions.
• Facilitate large stakeholder groups and manage the complete requirements lifecycle.
• Conduct structured analysis using both quantitative and qualitative techniques.
• Profile data manually and document transformation logic and rules.
• Produce clear and concise Source to Target Mapping Documents with detailed Data Mapping Requirements.
• Collaborate effectively within an Agile environment.
• Collaborate with data engineers, analysts, and business stakeholders to understand requirements.
• Ensure data accuracy, consistency, and governance across systems.

Lead Data Analyst - Consulting

Technical Program Manager

• Work on the latest applications of data engineering to solve complex business problems in the Healthcare domain.
• Lead and manage analytics programs focused on claims datasets.
• Collaborate directly with client stakeholders to translate business challenges into high-level analytics solution designs.
• Apply advanced analytics approaches, methodologies, and best practices to leverage data for informed decision-making.
• Translate strategy into action and simplify complex issues.
• Work closely with cross-functional teams to deliver high-value features.
• Present analytic and product solutions to business audiences.
• Participate in solution design discussions with team members.

Technical Program Manager – Health Insurance

Insurance

• Manage large-scale data analytics client engagements in a global delivery model
• Work with clients’ Technology and Program teams to craft the engagement planning and execution roadmap
• Work with business executives to understand business requirements and constraints for the success of the program.
• Present solutions to technology and business audiences highlighting the robustness of the solution and how it could help generate business value.
• Responsible for presentations to senior management and client executives, communicating results to client stakeholders, and developing plans to help operationalize the solutions.
• Ideating problem solutions leveraging modern trends in data and analytics solutions and related patterns
• Hold discussions on high-level architecture, platforms, and tools fitments for data and analytics solutions
• Steer discussions in strategizing and implementing digital and cloud solutions.
• Collaborating with business / IT stakeholders and product managers to ideate software

Senior Manager – Technology Consulting, P&C Insurance

Senior Manager/Associate Director – Technology Consulting, P&C Insurance

Supply Chain

• Lead and manage offshore teams delivering supply chain analytics and data migration initiatives
• Act as a bridge between business stakeholders and technical teams to ensure alignment on goals and deliverables
• Engage with senior client stakeholders (Director/VP level) to understand business context and define analytics solution approaches
• Break down complex problems into structured tasks across business analysis, data science, and engineering
• Guide and mentor teams of business analysts and data professionals to deliver high-impact solutions
• Architect and design scalable data analytics frameworks and solutions
• Apply advanced analytics techniques (Machine Learning, AI, Optimization, Operations Research) to solve business problems
• Develop and maintain predictive and prescriptive models in enterprise production environments
• Build and deploy simulation, optimization, and scenario planning tools for decision support
• Work with large datasets to extract, transform, and structure data using SQL and related tools
• Drive cross-functional collaboration to deliver innovative, data-driven solutions
• Ensure data quality, validation, and governance standards are maintained
• Translate complex analytical outputs into clear, actionable insights for business stakeholders

Manager, Senior Manager – Supply Chain Analytics, CPG/Retail

Manager

• Lead end-to-end delivery of data science and advanced analytics solutions in the commercial pharma domain.
• Design and implement Next Best Action (NBA) models to optimize customer engagement and decision-making.
• Develop and scale omnichannel analytics frameworks, integrating data from multiple touchpoints (CRM, digital, field, marketing).
• Partner with business stakeholders (Sales, Marketing, Market Access) to translate business problems into analytical solutions.
• Drive customer segmentation, targeting, personalization, and campaign optimization strategies.
• Build predictive models (propensity, churn, response, uplift modeling) to enhance commercial performance.
• Oversee data pipelines, model deployment, and monitoring in production environments.
• Mentor and lead a team of data scientists and analysts.
• Collaborate with data engineering teams to define data architecture, data quality standards, and governance frameworks for analytics use cases.
• Ensure alignment with regulatory and compliance standards within pharma.

Senior Manager – Omnichannel, Commercial Pharma

Machine Learning Engineer

• Implement scalable and reliable systems leveraging cloud-based architectures, technologies and platforms to handle model inference at scale.
• Deploy and manage machine learning & data pipelines in production environments.
• Work on containerization and orchestration solutions for model deployment.
• Participate in fast iteration cycles, adapting to evolving project requirements.
• Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications.
• Leverage CICD best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
• Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.
• Collaborate with Data scientists, software engineers, data engineers, and other stakeholders to develop and implement best practices for MLOps, including CI/CD pipelines, version control, model versioning, monitoring, alerting and automated model deployment.
• Manage and monitor machine learning infrastructure, ensuring high availability and performance.
• Implement robust monitoring and logging solutions for tracking model performance and system health.
• Monitor real-time performance of deployed models, analyze performance data, and proactively identify and address performance issues to ensure optimal model performance.
• Troubleshoot and resolve production issues related to ML model deployment, performance, and scalability in a timely and efficient manner.
• Implement security best practices for machine learning systems and ensure compliance with data protection and privacy regulations.
• Collaborate with platform engineers to effectively manage cloud compute resources for ML model deployment, monitoring, and performance optimization.
• Develop and maintain documentation, standard operating procedures, and guidelines related to MLOps processes, tools, and best practices.

ML Ops Architect

Data Scientist

• Work on the latest applications of data science to solve business problems
• Work directly with client stakeholders to translate business problems into high level analytics solution designs
• Present analytic solutions to business audiences highlighting robustness of the solution and how it could help generate business value
• Develop end-to-end solutions based on in-depth understanding of business problems to ensure analytics solutions are delivered efficiently, predictably, and sustainably
• Responsible for managing analytics projects, collaborating with client stakeholders and Tiger’s team situated globally
• Participate in discussions with team members to select and apply relevant analytic techniques and create actionable business insights
• Responsible for making presentations to senior management, communicating results to business teams, and develop plans to help operationalize analytic solution

Manager, Senior Manager – Data Science

• ML Engineer with 5-7 years of IT experience.
• Pipeline Training Models, Building, Deployment, Testing, and Monitoring using AWS SageMaker, AWS CFT, AWS CodePipeline, Lambda, etc.
• Develop Airflow DAGs to run training and scoring pipelines
• Develop a Testing framework with Pytest
• Implement monitoring solution with homebrew solution using Lambda and Dash
• Develop Data Quality solutions potentially leveraging Great Expectations.

MLOps Engineer

Product Manager

• Responsible for leading the development and enhancement of our infrastructure products and services.
• Drive the roadmap and ensure the successful delivery of innovative solutions to meet our customers' needs.
• Collaborate closely with cross-functional teams to define product strategies, prioritize features, and oversee the product development lifecycle.

Product Manager – Infrastructure

• - Own and execute the product vision, strategy, and roadmap for enabled solutions.
• - Define ‘what’ and ‘why’ the platform should build, and what business and user problems it will solve
• - Define and track product OKRs and KPIs, and drive the team to achieve them
• - Drive GTM (go to market) excitement and awareness of our platform across all enterprise teams
• - Collaborate closely with engineering and data science teams to build, validate, and showcase POCs and demos.
• - Translate business problems into clear product requirements and user stories.
• - Communicate product strategy, progress, and outcomes to senior leadership and key stakeholders.
• - Ensure products align with BFS regulatory, compliance, and risk considerations.

Product Manager – Analytics Consulting, Healthcare

• Lead the development and delivery of executive-ready Weekly Business Review (WBR) presentations for senior leadership (SLT)
• Conduct in-depth, ad hoc analyses across margin, sales performance, pipeline health, marketing effectiveness, and customer behavior
• Translate complex and ambiguous datasets into clear, actionable insights and strategic recommendations
• Act as a trusted advisor to client stakeholders, driving data-informed decision-making
• Partner with cross-functional teams across sales, marketing, finance, and digital to understand business needs and deliver impactful analytics solutions
• Design, build, and optimize dashboards using Looker and other BI tools to enable scalable reporting and self-service analytics
• Analyze digital user journeys using clickstream data to identify opportunities for optimization and growth
• Perform advanced marketing analytics, including CLTV modeling, cohort analysis, and campaign performance measurement
• Support sales and financial analytics, including P&L analysis, forecasting, and pipeline tracking
• Ensure high standards of data quality, integrity, and clarity in all deliverables

Manager, Senior Manager – Analytics Consulting

Financial Planning and Analysis

• Provide Thought Leadership and Client Engagement
• Liaise with all levels of data leaders at our strategic clients to provide forward looking advisory and thought leadership
• Hold brown bags and educative forums to share thought leadership with our core clients
• Hold client platform, organization and practices reviews proactively and provide improvement recommendations, solutions and roadmaps
• Provide technology leadership to transformation agendas rooted in Data and Insights, addressing Technology, Organization and Processes
• Help recruit key talent to fill key client technology roles
• Create detailed technology solutions for first of a kind and complex initiatives
• Pre- Sales and New business
• Present Tiger capabilities to new prospects
• Create Solutions and Proposals for client requirements
• Mentor Engineering Talent at different levels
• Manage architect resources
• Create thought leadership content for go to market offerings including reference architectures, methods and practices
• Interview and participate in helping evolve hiring practices
• Liaison with offshore counterparts on solutions, training and overall capabilities development

Technology Partner – Banking and Financial Services

Artificial Intelligence

• Act as a trusted advisor to C-level stakeholders, defining AI strategy, roadmaps, and transformation initiatives aligned to business goals
• Lead end-to-end delivery of AI/ML solutions, from data discovery and modeling to deployment, monitoring, and continuous improvement
• Architect and implement scalable data platforms (lakehouse, data mesh) and AI ecosystems leveraging cloud technologies (AWS, GCP, Azure)
• Drive advanced analytics, machine learning, and GenAI use cases, including NLP, forecasting, optimization, and recommendation systems
• Establish and scale MLOps practices, including CI/CD pipelines, model governance, observability, and lifecycle management
• Translate complex business requirements into technical specifications, including data models, STTM, and transformation logic
• Lead large, cross-functional teams across data engineering, data science, and analytics
• Ensure responsible AI practices, including model explainability, fairness, privacy, and regulatory compliance
• Identify new business opportunities, contribute to pre-sales, solutioning, and thought leadership
• Mentor senior talent and build high-performing AI and data teams

AI Tech Partner

Architect

• Lead modernization initiatives, transforming legacy systems into cloud-native, scalable architectures
• Design and architect API-first ecosystems, including RESTful and event-driven APIs
• Build and optimize event-driven architectures using messaging and streaming platforms
• Define and implement API gateways for routing, security, throttling, and monitoring
• Architect high-throughput, low-latency distributed systems with strong resiliency
• Design end-to-end full-stack solutions across frontend, backend services, APIs, and data layers
• Ensure strong security architecture, including authentication, authorization, encryption, and zero-trust principles
• Drive deployments across cloud (AWS/Azure/GCP) and on-prem/hybrid environments
• Ensure responsive, intuitive user experiences compatible with desktop and mobile platforms
• Establish architecture standards, scalability patterns, and performance benchmark