Sr. Analyst, Statistical Forecast & Accuracy

McKesson•Irving, TX

18h•Hybrid

About The Position

The Sr. Analyst, Statistical Forecast & Accuracy is an individual contributor within the Demand Planning team, reporting directly to the Consensus Demand Planning Lead. This role is the technical authority for the statistical forecast that underlies the consensus plan. The Sr. Analyst is accountable for ensuring the system produces a reliable, accurate baseline forecast across portfolios, and governing forecast performance over time. This role intervenes when the engine produces outputs that do not reflect reality, manages model parameters and bias across portfolios, and drives the continuous improvement discipline that makes the consensus forecast credible and defensible. This is a technical role that requires deep proficiency in statistical forecasting methods, hands-on expertise in Kinaxis Maestro, and the judgment to distinguish a model problem from a demand reality problem. The Sr. Analyst must be able to translate statistical findings into clear, actionable guidance for the Consensus Demand Planning Lead.

Requirements

Degree or equivalent and typically requires 7+ years of relevant experience.
Bachelor's degree in Supply Chain, Statistics, Data Science, Mathematics, or a related quantitative field strongly preferred or equivalent experience.
7+ years in demand planning, supply chain analytics, or a related field, with demonstrated hands-on experience owning statistical forecast configuration and model governance in a production planning environment.
Direct experience working in Kinaxis Maestro, including parameter configuration, model selection, and demand planning engine operation.
Demonstrated ability to diagnose and correct statistical forecast failures, including history cleansing, outlier management, and model parameter adjustment.
Proven ability to build and maintain a forecast accuracy and bias measurement framework, including root cause analysis and continuous improvement tracking.
Configuring, maintaining, and intervening in Kinaxis Maestro to ensure the statistical forecast engine produces reliable outputs across all portfolios.
Applying disciplined cleansing and data preparation so that the statistical baseline reflects real demand patterns rather than data noise.
Diagnosing forecast failures with precision, distinguishing model problems from enrichment problems from genuine demand variability, and routing accountability correctly.
Building and sustaining a bias management discipline that improves forecast quality over time rather than accepting persistent error as unavoidable.
Operating as the technical anchor for a team that is otherwise oriented toward commercial and market judgment, without creating a bottleneck or a knowledge silo.

Nice To Haves

Experience in a comparable advanced planning system (SAP IBP, Blue Yonder, o9) considered with a clear expectation of rapid Maestro proficiency.
Experience operating within a structured S&OP or consensus planning process, including preparing performance reporting for cross-functional review.
Familiarity with pharmaceutical, healthcare, or wholesale distribution demand dynamics is a strong plus, particularly experience with product lifecycle events, NDC switches, and new business onboarding that create history anomalies.
Comfort operating in ambiguity and building process structure where governance is still maturing, particularly in the context of a large-scale system transition where baselines and benchmarks do not yet exist.
Ability to communicate technical findings clearly to non-technical stakeholders, including explaining why the system produced a wrong output and what was done to fix it.

Responsibilities

Own the configuration and maintenance of statistical forecast parameters in Kinaxis Maestro, including model selection and parameter tuning.
Apply history cleansing disciplines prior to each forecast cycle, adjusting for outliers, customer losses, NDC switches, and new product launches to ensure the statistical baseline reflects true underlying demand patterns.
Intervene directly when the system produces outputs that are wrong: update model parameters, alter model selection, and document the rationale for every change so that interventions are traceable and auditable.
Serve as the primary point of contact for the COE on statistical forecast configuration, parameter governance, and planning engine integrity questions.
Own the forecast accuracy and bias measurement framework across all portfolios, tracking MAPE, bias, and error metrics at the appropriate level of granularity to surface actionable insights rather than just headline numbers.
Conduct structured root cause analysis on forecast misses, distinguishing between model failure, enrichment failure, and genuine demand variability, and communicating findings clearly to the Consensus Demand Planning Lead and Channel Leads.
Identify and flag systematic bias in the statistical forecast, whether directional over- or under-forecasting by product type, channel, or time horizon, and drive model adjustments to correct it.
Maintain a continuous improvement cadence, tracking accuracy trend over time and holding the team accountable to improving against a defined baseline as the system matures and data quality improves.
Partner with Channel Leads to determine whether a forecast miss originated in the statistical baseline or in enrichment inputs, and prepare forecast performance reporting for S&OP reviews facilitated by the COE, ensuring accuracy findings are visible to cross-functional participants and tied to operational implications for supply and inventory.