Technical Program Manager

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

<p>Required:</p><ul><li>5+ years of applied forecasting and data domain experience.</li><li>Proven experience leading at least two comparable forecasting engagements from discovery through production/handover.</li><li>Strong experience with comparable-unit / same-store-style forecasting approaches.</li><li>Expert-level multivariable regression, collinearity analysis, VIF interpretation, model tuning, selection, and holdout validation.</li><li>Strong understanding of sparse and zero-inflated datasets; must understand why nulls cannot be silently treated as zero.</li><li>Strong metric fluency, including R², WAPE, MAPE, and p-values, with the ability to explain metric selection in business language.</li><li>Deep understanding of data leakage and point-in-time feature correctness.</li><li>Strong Python and SQL skills and experience creating reproducible analytical workflows/notebooks.</li><li>Git and pull-request-based software delivery experience.</li><li>Strong Data Architecture and Solution Architecture capability across ingestion, transformation, modeling, security, governance, APIs, cloud, and production operations.</li><li>Strong understanding of ML, GenAI/LLMs, RAG, AI agents, and modern enterprise AI patterns.</li><li>Proven executive communication skills; must be able to present to non-statistical audiences using business outcomes first and methodology second.</li><li>Must be capable of explicitly communicating forecast/model limitations, uncertainty, and risk in plain language.</li><li>Proven client consulting and thought-leadership experience; able to teach methodology, not simply execute it.</li><li>Demonstrated experience leading senior client workshops, technical discovery, architecture discussions, and executive readouts.</li><li>Strong commercial judgment and ability to connect technical decisions to revenue, operational, or customer outcomes.</li><li>Preferred domain experience in sports, media, entertainment, streaming, ticketing, subscriptions, sponsorship, advertising, telecom, or commercial/revenue analytics.</li></ul><p>Desired:</p><ul><li>Hierarchical/mixed-effects modeling, ADRs, large-scale categorical encoding, MLOps, governance, and responsible AI experience.</li><li>Strong Snowflake experience; Snowflake ML / Model Registry experience.</li><li>10–12+ years overall Data, Analytics, AI, Architecture, or related technology experience.</li></ul>