Senior Data Architect (Pre-sales & Solutions)

About the role

Technology is our how. And people are our why. For over two decades, we have been harnessing technology to drive meaningful change. By combining world-class engineering, industry expertise and a people-centric mindset, we consult and partner with leading brands from various industries to create dynamic platforms and intelligent digital experiences that drive innovation and transform businesses. From prototype to real-world impact - be part of a global shift by doing work that matters.

Responsibilities

• Serve as a senior trusted advisor to CIO, CTO, CDO, CRO, business, product, and technology stakeholders.
• Lead Data & AI discovery and translate ambiguous business problems into measurable, production-ready solutions.
• Design end-to-end architectures spanning data platforms, analytics, machine learning, GenAI, applications, APIs, governance, and integrations.
• Lead and challenge advanced forecasting and predictive-modeling approaches, including regression, sparse and zero-inflated data, feature design, model selection, tuning, and validation.
• Define appropriate business and model success measures, including R², WAPE, MAPE, statistical significance, and business-impact KPIs.
• Ensure point-in-time correctness, prevent data leakage, and maintain rigorous model-development and validation practices.
• Provide hands-on technical leadership using Python, SQL, Snowflake, notebooks, Git, and modern Data/AI platforms.
• Shape AI use cases across forecasting, sponsorship sales, lead scoring, next-best-action, revenue intelligence, personalization, subscription growth, and commercial optimization.
• Evaluate when classical analytics/ML, GenAI, or agentic AI is the appropriate solution.
• Lead architecture and solution-design workshops and present recommendations to senior and executive audiences.
• Support proposals, SOWs, RFI/RFP responses, estimates, staffing models, and technical solution shaping.
• Lead and mentor multidisciplinary teams across Data Science, Data Engineering, AI Engineering, Software Engineering, and Architecture.
• Actively participate in client stand-ups, backlog refinement, executive readouts, and strategic planning.
• Teach and transfer knowledge to client teams; documentation, reproducibility, and handover are expected parts of delivery.
• Challenge client requests when the proposed approach does not solve the underlying business problem.

Requirements

• 5+ years of applied forecasting and data domain experience.
• Proven experience leading at least two comparable forecasting engagements from discovery through production/handover.
• Strong experience with comparable-unit / same-store-style forecasting approaches.
• Expert-level multivariable regression, collinearity analysis, VIF interpretation, model tuning, selection, and holdout validation.
• Strong understanding of sparse and zero-inflated datasets; must understand why nulls cannot be silently treated as zero.
• Strong metric fluency, including R², WAPE, MAPE, and p-values, with the ability to explain metric selection in business language.
• Deep understanding of data leakage and point-in-time feature correctness.
• Strong Python and SQL skills and experience creating reproducible analytical workflows/not