• Build and deploy advanced predictive models for lead scoring, agent matching, price and time-on-market estimation, and conversion likelihood. • Develop production-grade RAG and GraphRAG pipelines, construct complex knowledge graphs across listings, agents, and clients, and design retrieval and ranking systems for unstructured data. • Write robust data transforms in Databricks (using SQL and PySpark) and author highly performant SQL queries across ClickHouse and PostgreSQL. • Partner closely with engineering teams to ship model and LLM outputs to production environments, frequently utilizing Inngest. • Analyze MLS data to surface actionable market trends and directly feed data-driven product features. • Design and analyze product experiments utilizing PostHog, and build comprehensive dashboards and analyses within Hex and Metabase. • Act as a strategic partner by translating ambiguous business questions into clear, actionable, and data-backed recommendations.

Role Description 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. - 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. Qualifications - Proven experience 10+ years working as a Data Scientist, with a focus on supply chain optimization and inventory allocation. - MS or PhD in Computer Science, Operations Research, Applied Mathematics, Machine Learning, or a related field. - Experience with using mathematical programming solvers such as Gurobi, Xpress MP, CPLEX, or Google OR Tools in applications. - Solid understanding of statistical methods, optimization techniques, and predictive modelling concepts. - Strong proficiency in programming languages such as Python, Pyspark and SQL, and experience working with data analysis and machine learning libraries. - Ability to apply various analytical models to business use cases. - Exceptional communication and collaboration skills to understand business partner needs and deliver solutions and explain to business stakeholders. Benefits This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility. Company Description 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. - Market leaders in AI and analytics consulting in the CPG & retail industry. - Over 40% of our revenues coming from the CPG & retail sector. - Fastest-growing sector with an emphasis on beefing up talent.

• Act as Lead Clinical Data Manager for assigned data management projects • Act as a Project Lead for Biometric-only projects • Coordinate work of other Clinical Data Management staff assigned to the project • Review Study Protocol • Develop CRF or review of sponsor-generated CRFs • Develop Database • Define and program edit checks • Responsible to prepare, finalize and archive all related DM documentation • Develop Data Management Plan and Data Management Report • Develop Data Validation Plan (including definition of edit checks) • Develop Data Entry Instructions/ eCRF completion guidelines • Coordinate validation of database, edit checks and trial and site settings • Performs validation of database, edit checks and trial and site settings in studies where not the responsible CDM • Perform CRF handling including logging, tracking and sorting • Support the CRAs in study related questions • Perform Data cleaning and query handling • Ensure completion of Quality Control task for the finalization and transfer of the study database and related documents to the sponsor • May coordinate SDTM mapping with the senior programmer when needed • Carry out all tasks related to database closure • Provide CDs with study documentation to the sites (patient data, audit trail and closed queries) • File/archive essential documents • Provide metrics and status updates to senior management and project leaders and sponsors if required • Train and support Clinical Data Associates and Clinical Data Managers • Train and support both internal and external Clinical Study Teams • Represent TFS Data Management in sponsor and investigator meetings • Responsible to escalate any issues on projects or the need for potential SOP deviations • May perform system validation, create test scripts for system validation as well as perform UAT of ePROs in collaboration with the CRA • Actively contribute to the organization and development of processes to enhance the work at TFS

• Own the customer value narrative: Build and maintain the infrastructure that lets every customer (and our sales team) articulate Verify's ROI in dollars and percentages • Ship production ML systems: From feature engineering through deployment, monitoring, and iteration • Create reliable, self-serve data products: Dashboards, APIs, and datasets that scale without manual intervention • Drive pricing and packaging decisions: Provide the quantitative foundation for how we charge and what we bundle • Partner across the organization: Work with Product, Engineering, Finance, Sales, and Customer Success to embed data into every decision • Report to leadership: Own KPI narratives on margin drivers, growth levers, and competitive positioning