• Lead data management for complex, global clinical trials from study setup to database lock • Serve as the main client contact for all data management deliverables • Oversee SAE, PK, and external data reconciliation and database deployment • Manage timelines, budgets, and resource allocation to meet customer expectations • Mentor junior data management staff and promote best practices • Drive process improvements and ensure regulatory and SOP compliance

• Coordinate and align data products across business domains, ensuring consistency in priorities, ownership, and delivery expectations while establishing clear accountability • Serve as the primary coordination point between business stakeholders and data delivery teams, driving execution across a matrixed organization and influencing teams to deliver shared outcomes • Lead intake, prioritization, and demand management of data initiatives, aligning organizational priorities with capacity and maintaining visibility into dependencies and risks • Orchestrate end-to-end delivery across Data Ingestion, Data Engineering, and Data Distribution functions, resolving dependencies and ensuring consistent execution • Establish communication frameworks, governance structures, and reporting mechanisms that provide transparency into portfolio health and delivery metrics • Lead the Data Product Coordination function, establishing processes, operating model standards, and performance management discipline across matrixed teams • Build and develop a team of Data Product Coordinators aligned to specific business domains (Clinical, Corporate, Commercial), ensuring they develop deep domain expertise and act as primary alignment points between business and technical teams • Coach and develop talent, establishing clear roles, accountability, and performance expectations while building strong domain, delivery, and stakeholder management capabilities

• Lead the development and deployment of advanced analytics, AI/ML, GenAI, and digital solutions that transform Global Supply Chain operations and enable reliable delivery of critical medicines to patients worldwide. • Design and deploy AI, GenAI, Agentic, and advanced analytics solutions to transform end-to-end Supply Chain operations. • Lead the development of scalable digital products, analytical applications, and automation capabilities using large and complex datasets. • Help establish and scale a Global Supply Chain Digital capability team in India, including organizational design, operating model development, governance, and delivery processes. • Provide leadership and oversight for global teams of data scientists and digital developers across onshore and offshore locations. • Partner with business leaders, IT, and data engineering teams to deliver robust, scalable, and business-aligned analytics solutions. • Develop impactful visualizations and executive-level insights that support strategic decision-making and operational transformation. • Explore and implement emerging technologies and modern data science practices to enhance organizational capabilities. • Lead productivity analytics initiatives and develop KPIs and performance metrics that drive operational excellence across the global supply chain. • Mentor and develop Digital/Data Science staff while fostering a culture of innovation, collaboration, and continuous improvement.

Role Description The Data Science team at Block turns insights from our unique datasets into actions that improve the customer experience every day. In this role, we're looking for a Data Scientist to own the modeling and experimentation at the core of how Square prices globally. You'll build the elasticity and willingness-to-pay models, design and run the pricing experiments, and stand up the analytical infrastructure that makes pricing measurable and controllable — shaping pricing strategy and deal-desk automation through the models and experiments you build. You Will - Model price elasticity and willingness-to-pay across segments, geographies, and payment methods, and quantify the trade-off between margin, conversion, and merchant retention. - Design, run, and read out pricing experiments (A/B, difference-in-differences, and bandit-based dynamic tests) and translate results into recommendations that shape strategy. - Decompose merchant economics across interchange, scheme, and risk-cost layers to identify where pricing can flex and where it can't. - Build the pricing intelligence that powers Square's agentic deal tooling (DealBot) — rate recommendations, ROI and pre-approval logic, guardrail configurations, and mispricing detection — so quotes are fast, accurate, and within guardrails at scale. - Evaluate and monitor the AI systems you ship — pre-deployment testing for accuracy, boundary and edge cases, and bias in rate recommendations, and in-production monitoring for accuracy, drift, and mispricing — so agentic pricing tools stay reliable as the business changes. - Own end-to-end execution across the stack — analysis, pipeline, ETL, experimentation, and visualization. - Approach problems from first principles, using a variety of statistical and modeling techniques to understand customer behavior and price response. - Build and maintain the pricing analytics the team relies on — price realization, margin leakage, discount-waterfall, and win/loss analyses — as self-serve dashboards and curated datasets. - Measure the impact of AI-driven pricing automation with causal methods (interrupted time series, difference-in-differences) on deal velocity, quote acceptance, and margin. - Write code to process, cleanse, and combine data sources into curated ETL datasets easily used by the broader team. - Partner closely with cross-functional stakeholders across Finance, Risk, Product, and go-to-market teams, translating complex technical and AI concepts clearly for non-technical audiences. Qualifications - A bachelor degree in statistics, data science, economics, or similar STEM field with 7+ years of experience in a relevant role OR a graduate degree in statistics, data science, economics, or similar STEM field with 5+ years of experience in a relevant role. - Fluency in causal inference and experimentation, with hands-on experience modeling price elasticity or willingness-to-pay. - Prior exposure to a pricing-adjacent domain a strong plus — risk-based pricing (payments, lending, insurance), pricing science, or deal pricing analytics. - Advanced proficiency with SQL and data visualization tools (e.g. Tableau, Looker, etc). - Experience with scripting and data analysis programming languages, such as Python or R, including using them to evaluate AI system behavior. - Gone deep with cohort and funnel analyses, with a solid understanding of statistical concepts such as selection bias, probability distributions, and conditional probabilities. - Comfort leveraging AI tools to accelerate modeling and analysis, and a working understanding of generative AI architectures — LLMs, RAG systems, and agentic AI; experience building, testing, or evaluating LLM-powered systems in production a strong plus. Requirements - Technologies We Use and Teach: SQL, Snowflake, etc. - Python (Pandas, Numpy). Benefits - Remote work. - Medical insurance. - Flexible time off. - Retirement savings plans. - Modern family planning.