Senior Data Architect (Pre-sales & Solutions)

- 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.

Required:

- 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/notebooks.

- Git and pull-request-based software delivery experience.

- Strong Data Architecture and Solution Architecture capability across ingestion, transformation, modeling, security, governance, APIs, cloud, and production operations.

- Strong understanding of ML, GenAI/LLMs, RAG, AI agents, and modern enterprise AI patterns.

- Proven executive communication skills; must be able to present to non-statistical audiences using business outcomes first and methodology second.

- Must be capable of explicitly communicating forecast/model limitations, uncertainty, and risk in plain language.

- Proven client consulting and thought-leadership experience; able to teach methodology, not simply execute it.

- Demonstrated experience leading senior client workshops, technical discovery, architecture discussions, and executive readouts.

- Strong commercial judgment and ability to connect technical decisions to revenue, operational, or customer outcomes.

- Preferred domain experience in sports, media, entertainment, streaming, ticketing, subscriptions, sponsorship, advertising, telecom, or commercial/revenue analytics.

Desired:

- Hierarchical/mixed-effects modeling, ADRs, large-scale categorical encoding, MLOps, governance, and respon