Tiger Analytics

Tiger Analytics is pioneering what AI and analytics can do to solve some of the toughest problems faced by organizations globally. We develop bespoke solutions powered by data and technology for several Fortune 100 companies. We have offices in multiple cities across the US, UK, India, and Singapore, and a substantial remote global workforce. We are also market leaders in AI and analytics consulting in the CPG & retail industry with over 40% of our revenues coming from the sector. This is our fastest-growing sector, and we are beefing up our talent in the space. We are looking for a Senior Data Scientist with a good blend of data analytics background, practical experience in Operation research strategies and Pricing Analytics within supply chains, and strong coding capabilities to add to our team. **Key Responsibilities:** - Responsible for refactoring the Optimization algorithm written in Python using Object Oriented Programming - Work on the latest applications of data science to solve business problems in the Supply chain and optimization space of Retail and/or CPG. - Utilize advanced statistical techniques and data science algorithms to analyze large datasets and derive actionable insights related to Pricing Optimization. - Develop and implement predictive models and optimization algorithms to improve Pricing techniques. - Collaborate with cross-functional teams to understand business requirements and translate them into data-driven solutions. - Stay abreast of industry trends and best practices in data science, replenishment optimization, and supply chain management, and leverage this knowledge to drive innovation within the organization. - Collaborate, coach, and learn with a growing team of experienced Data Scientists.

- Responsible for refactoring the Optimization algorithm written in Python using Object Oriented Programming - Work on the latest applications of data science to solve business problems in the Supply chain and optimization space of Retail and/or CPG. - Utilize advanced statistical techniques and data science algorithms to analyze large datasets and derive actionable insights related to Pricing Optimization. - Develop and implement predictive models and optimization algorithms to improve inventory management, reduce stockouts, and optimize resource allocation across the supply chain. - Collaborate with cross-functional teams to understand business requirements and translate them into data-driven solutions. - Design and execute experiments to evaluate the effectiveness of different replenishment strategies and allocation policies. - Monitor and analyze key performance indicators (KPIs) related to replenishment and supply chain allocation, and provide recommendations for continuous improvement. - Stay abreast of industry trends and best practices in data science, replenishment optimization, and supply chain management, and leverage this knowledge to drive innovation within the organization. - Collaborate, coach, and learn with a growing team of experienced Data Scientists.

• The Forward Deployment Engineer (FDE) drives the on-site deployment, integration, and scaling of our enterprise Generative AI solutions. • This role embeds directly within customer engineering teams to operationalize Large Language Models (LLMs) and retrieval systems across multi-cloud environments (AWS, Azure, GCP). • You will bridge the gap between AI research and production-grade cloud infrastructure. • You will collaborate with cross-functional teams and business partners and will have the opportunity to drive current and future strategy by leveraging your analytical skills as you ensure business value and communicate the results.

• Work directly with business stakeholders and client teams to understand operational challenges, map existing business processes, and identify automation opportunities. • Lead end-to-end business process mapping initiatives using BPMN methodologies and Camunda workflow orchestration tools. • Create deterministic process maps, define subprocesses, establish operational guardrails, and design scalable workflow automation solutions. • Build and implement AI-driven automation solutions using AI automation tools and agentic AI frameworks. • Collaborate with client stakeholders to translate business problems into high-level analytics, automation, and AI solution approaches. • Drive customer-facing discussions, workshops, and consulting engagements with strong communication and presentation capabilities. • Present analytical and automation solutions to senior business audiences, highlighting business impact, scalability, and operational efficiency gains. • Participate in process transformation and workflow optimization initiatives across Operations and Finance functions. • Work with globally distributed analytics, IT, and Data Engineering teams to operationalize automation and analytics solutions within enterprise systems. • Contribute to solution design discussions by applying relevant analytical, automation, and process optimization techniques. • Manage analytics and automation projects end-to-end including stakeholder communication, execution tracking, governance, and delivery management. • Leverage process mining tools to analyze business workflows, identify inefficiencies, and recommend automation opportunities.

• Provide Thought Leadership and Client Engagement. • Liaise with data leaders at strategic clients for advisory and insights. • Hold educational forums to share thought leadership with core clients. • Conduct client platform and practices reviews with improvement recommendations. • Provide technology leadership for transformation agendas rooted in Data and Insights. • Recruit key talent for client technology roles. • Create technology solutions for complex initiates. • Engage in pre-sales and new business development. • Present Tiger capabilities to new prospects. • Develop solutions and proposals for client requirements. • Mentor Engineering Talent at different levels and manage architect resources. • Create thought leadership content for market offerings.

• Lead bi-weekly allocation performance reviews with brand planning teams, covering service level, sales, GM$, GM%, GMROI, sell-through, and allocation accuracy. • Present KPI diagnostics with root-cause analysis: not just “what happened” but “why it happened” and “what we’re doing about it.” • Produce quarterly business reviews for executive leadership that synthesize allocation performance trends and strategic recommendations. • Leverage existing dashboards and alerting tools to monitor allocation outcomes across all brands at the store-SKU level. • Where tools are lacking, work with technical team to get the artifact built or enhanced. • Identify emerging issues before they become problems: sell-through falling behind pace, inventory building in the wrong stores, stockout patterns that signal a model or grading issue. • Decompose KPI misses into demand-side drivers (forecast accuracy, trend shifts) and supply-side drivers (misallocation, replenishment lag, pack constraints) to pinpoint actionable root causes. • Analyze outcomes across product lifecycle types (basics, seasonal basics, fashion) and tailor diagnostic frameworks to each. • Translate diagnostic findings into specific vendor improvement tickets with clear success criteria — push the vendor to improve their model where the data warrants it. • Define requirements for in-house diagnostic tools and work with the technical team to prioritize development. • Advise brand planning teams on allocation strategy decisions: store eligibility, DC holdback levels, size curve adjustments, replenishment throttling for end-of-season. • Own the vendor accountability framework: maintain the RACI, track vendor action rates, and escalate when commitments are not met. • Operate on a proactive communication cadence: reach out to stakeholders with findings and recommendations, don’t wait for them to ask. • Build trust as a fiduciary partner — the brands should believe you are watching their business and will advocate for their outcomes. • Manage the narrative during the transition from the prior consulting model: demonstrate early wins, set realistic expectations, and build confidence in the new capability.

• 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

• 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.

• 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.

- 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.