Tiger Analytics

AI Engineer

• Providing solutions for the deployment, execution, validation, monitoring, and improvement of MLE solutions
• Creating Scalable Machine Learning systems .
• Building reusable production data pipelines for implemented machine learning models
• Writing production-quality code and libraries that can be packaged as containers, installed and deployed
• Collaborating with cross-functional teams and business partners to drive current and future strategy by leveraging analytical skills

Account Executive

• Drive new business pipeline and closed revenue through joint selling with Microsoft account teams.
• Identify and pursue co-sell eligible opportunities across Retail/CPG, Manufacturing, and Energy.
• Expand Tiger’s presence within existing enterprise accounts by building strong relationships with Microsoft account teams, industry advisors/execs and aligning capabilities to Microsoft’s industry & customer priorities.
• Own quarterly revenue targets tied to Microsoft-sourced and Microsoft-influenced pipeline.
• Partner with industry and delivery leaders to shape proposals and customer-ready materials.
• Build trusted relationships with Microsoft field sellers, industry leaders, and partner teams.
• Drive top-of-mind awareness of Tiger’s capabilities across Microsoft’s industry teams.
• Participate in Microsoft account planning cycles and industry councils.
• Ensure Tiger is represented in Microsoft’s internal deal orchestration motions.
• Map Tiger’s industry capabilities to Microsoft’s industry priorities and solution plays.
• Develop joint value propositions and co-sell plays tailored to target industries.
• Partner with marketing to build co-branded assets and event strategies.
• Identify gaps in Tiger’s offerings and collaborate to shape new accelerators.
• Leverage Microsoft partner programs and incentives to build and accelerate the pipeline.
• Ensure opportunities are properly registered and progressed through the Microsoft partner portal.
• Track and report on pipeline, revenue influence, and field engagement metrics.
• Drive alignment with Microsoft’s FY priorities and scorecards.

Partner Sales Executive – Microsoft Alliance

Chief Technology Officer

• Identify and pursue new business opportunities in the AI & ML space.
• Develop and execute strategies to expand our client base and market presence.
• Build and maintain strong relationships with key stakeholders and clients.
• Collaborate with sales and marketing teams to create compelling value propositions and proposals.
• Lead the design, development, and deployment of enterprise-level data science solutions.
• Ensure solutions are scalable, robust, and aligned with business objectives.
• Stay abreast of the latest advancements in AI & ML technologies and integrate them into our offerings.
• Mentor and guide a team of data scientists and engineers to achieve project goals.
• Develop and implement a strategic roadmap for data science initiatives.
• Align data science projects with overall business strategy and client needs.
• Drive innovation and continuous improvement in data science methodologies and practices.
• Work closely with clients to understand their business challenges and requirements.
• Translate client needs into actionable data science solutions.
• Present findings, insights, and recommendations to clients in a clear and compelling manner.

Lead DS/AI Tech

QA Engineer

• Own and drive end-to-end sales cycles for large QE opportunities (USD 5M+)
• Consistently achieve and exceed annual QE revenue targets
• Build and maintain a strong pipeline of exclusive and sole-sourced QE deals
• Drive long-term managed services, annuity-based, and transformation-led QE engagements
• Collaborate with the account Client Partners and enable channels for QE
• Creating RFP, RFI, and RFQ responses for QE opportunities
• Shape differentiated QE solution strategies covering automation, AI-driven QE, QE for AI, performance engineering testing, data testing, and continuous testing
• Drive pricing, commercials, value engineering, and deal structuring
• Collaborate closely with capability/practice and delivery teams to create winning proposals
• Build trusted advisor relationships with CXOs, CIOs, CTOs, Heads of Engineering, and Quality Engineering Leaders
• Act as executive sponsor for strategic QE accounts
• Lead executive presentations and contract negotiations
• Position QE as a business enabler and AI-led transformation driver
• Represent the organization in industry QE forums, conferences, roundtables, and client events
• Publish thought leadership content, including blogs, whitepapers, and articles on Quality Engineering, AI in testing, and digital quality trends
• Partner with the India practice & capability team and marketing teams on QE branding, campaigns, analyst interactions, and GTM initiatives
• Contribute to QE marketing strategies and market messaging
• Identify emerging QE trends and influence service offerings and roadmap with the capability development team

Sales Leader, Quality Engineering Services

Data Scientist

• - Serve as a resource for creating AI agents and RAG databases to solve problems. Develop AI Agent tools to automate the retrieval, wrangling, and analysis of data.
• - Utilize combined knowledge of data structures, analytics, algorithms/models, and strong computer science fundamentals to prepare datasets, conduct analytics, and develop deployable solutions with guidance from more senior resources.
• - Able to analyze and prepare complex and new data sources and incorporate them into analytical solutions.
• - Collaborate with customers and key stakeholders on initiating ideas, performing design, analysis, and process improvement.
• - Develop and deploy AI and ML solutions on Google Cloud Platform.
• - Utilize massive data sources to craft business insights and features for innovative solutions.
• - Understand diverse data sources, both structured and unstructured.

Senior Data Scientist – Agentic AI

Manager

• Design and develop Power BI dashboards and reports for commercial pharma (sales, field force, HQ reporting)
• Translate business requirements into data models, data flows, and scalable BI solutions
• Lead/assist in Power BI migration initiatives and reporting automation (Alteryx or similar tools)
• Ensure data quality, validation, and governance across reporting layers
• Collaborate with cross-functional teams and stakeholders; manage Agile delivery and support enhancements, including chatbot/AI integrations

Manager, Senior Manager – BI Consulting, Life Sciences, Pharma

• Lead the design, development, and enhancement of forecasting models to improve prediction accuracy and business outcomes.
• Build and deploy advanced **Deep Learning** models for large-scale structured and unstructured datasets.
• Develop **Generative AI / Explainable AI** solutions to provide transparent and interpretable insights from predictive models.
• Analyze healthcare datasets including claims, patient, provider, operational, or clinical data.
• Collaborate with business stakeholders, product teams, and engineering teams to translate business challenges into scalable AI solutions.
• Monitor model performance, retrain models, and optimize algorithms for production environments.
• Mentor junior data scientists and provide technical leadership across the project.
• Ensure compliance with healthcare data privacy and governance standards.

Lead Data Scientist – Healthcare

Platform Engineer

• Develop bespoke solutions powered by data and technology for several Fortune 100 companies.
• Solve business problems using systems thinking, AI engineering, and data-driven insights.
• Collaborate across solution architecture, consulting, and forward deployed engineering teams.

Senior Data Engineer, Platform Engineer – GCP

Insurance

• Tiger Analytics is an advanced analytics consulting firm recognized for our deep expertise in Data Science, Machine Learning, and AI. Our partnerships with Fortune 100 companies enable us to tackle complex business challenges and drive value through innovative analytical solutions. ****We are looking for an experienced Product Manager to lead the strategy, development, and go-to-market execution of data products within the Healthcare Payer domain. This role requires strong product ownership, stakeholder management, and hands-on collaboration with engineering and analytics teams.**
• **Responsibilities**-
• - 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.

Associate Director – Data Products, Health Insurance

• Analyze large-scale manufacturing data, including **sensor, batch, recipe, and production line data**
• Develop analytics solutions to identify **defects, scrap, rework, and process deviations**
• Perform **root cause analysis** and **multivariate process analysis** to uncover drivers of quality issues
• Build **anomaly and defect detection models** to proactively identify process failures
• Partner with manufacturing, quality, and operations teams to translate findings into actionable improvements
• Deliver measurable outcomes such as **cost reduction, waste minimization, and quality improvement**

Senior Data Scientist – NQC Reduction, Manufacturing Quality

• Work on the latest applications of data science to solve business problems in the optimization space for a CPG client
• Effectively communicate the analytics approach and how it will meet and address objectives to business partners.
• Product Roadmap Development: Create, manage, and communicate a clear roadmap and strategic vision for the pricing and promotion optimization product, aligned with business goals and market trends. Key activities include developing, maintaining, enhancing and scaling the product to various Markets.
• Quarterly Planning: Design and implement quarterly plans, ensuring alignment with product goals, KPIs, and timelines.
• Team Leadership: Lead and mentor a cross-functional team of 5-6 members, fostering a collaborative and high-performance environment.
• Pricing & Promotion Strategy: Define and implement an optimization framework to optimize pricing and promotional Strategies.
• Stakeholder Collaboration: Work closely with internal teams, including Marketing, Sales, Engineering, and Analytics, to gather requirements and translate them into actionable plans.
• Market and Competitive Analysis: Conduct ongoing analysis of market trends and competitor strategies to identify opportunities and gaps.
• Performance Monitoring: Track and analyze key metrics to ensure product success, and identify areas for improvement to drive product growth.
• Collaborate, coach, and learn with a growing team of experienced Data Scientists.
• Stay connected with external sources of ideas through conferences and community engagements.
• Support demands from regulators, investor relations, etc., to develop innovative solutions to meet objectives utilizing cutting-edge techniques and tools.

Senior Data Scientist - Pricing Optimization

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.