Sr Analyst, Supply Chain Planning

Blue Nile•New York, NY

About The Position

The Analyst, Supply Chain Planning provides the analytical foundation for demand, supply, and inventory decisions and drives improvements to the organization’s planning capabilities. Working alongside demand and supply planners, the role owns analysis, diagnostics, and reporting that make forecasting and replenishment decisions more accurate, timely, and defensible. The analyst builds and automates recurring performance reporting, investigates variance and its causes, and translates complex datasets into recommendations for leadership. An initial priority is developing store sales performance and replenishment analytics as Blue Nile expands its retail showroom footprint alongside e-commerce. The role establishes visibility into location-level sales and availability, identifies where inventory constraints limit sales, and develops repeatable approaches for allocation and replenishment across the network. The Planning team operates with a Python-native analytics stack; fluency in SQL, Python, and BI tooling is central to the work. The position supports forecasting, replenishment, inventory strategy, store analytics, and continuous improvement. It partners with planning and cross-functional teams, prepares analysis for weekly planning and monthly IBP cycles, automates recurring work, and communicates findings to technical and non-technical audiences. The role offers broad exposure across planning disciplines and potential growth into planning or analytics leadership as the team’s scope expands.

Requirements

At least 3 years of experience in supply chain planning, demand planning, inventory analytics, business analysis, or a comparable analytical role.
Demonstrated experience working with large, imperfect datasets and producing analysis that withstands scrutiny.
Bachelor’s degree in Supply Chain, Business, Economics, Statistics, Analytics, Industrial Engineering, Data Science, or a related field.
Fluency in SQL, Python, and BI tooling.
Analytical rigor and attention to detail, including reconciling data before reporting.
Intellectual curiosity and ability to investigate root causes.
Clear communication skills to translate technical analysis into actionable business recommendations.
Business acumen to connect operational metrics to commercial and financial outcomes.
Ownership mindset and initiative in improving processes.
Process orientation and a continuous improvement mindset.
Comfort with ambiguity and ability to prioritize competing demands in a fast-paced environment.
Collaborative approach across planning, commercial, and technology teams.

Nice To Haves

Exposure to forecasting, replenishment, or inventory management concepts.
Experience with retail store replenishment, allocation, or store performance analytics.
Experience in e-commerce, direct-to-consumer, retail, luxury goods, or premium consumer brands.
A track record of identifying and delivering process or reporting improvements.
A master’s degree in a quantitative discipline.
APICS CPIM or CSCP certification.
Certification or formal coursework in data analytics, statistics, or machine learning.

Responsibilities

Build, automate, and maintain recurring planning reports and dashboards covering forecast accuracy and bias, service level and fill rate, inventory turns, weeks of supply, replenishment health, and fulfilment mix.
Extract, clean, reconcile, and validate data from multiple source systems to produce trustworthy, decision-ready reporting.
Define and track planning KPIs against targets and develop exception reporting to surface risks early.
Investigate variances to root cause, distinguishing demand signals from data quality issues, seasonality, and one-off effects.
Prepare analytical inputs and supporting materials for weekly planning cycles and the monthly IBP cadence.
Partner with demand and supply planners by providing data, backtests, and diagnostics for forecasting and replenishment decisions.
Quantify forecast value add, benchmark model output against naive baselines, and identify cases where statistical output may require human override.
Analyze demand patterns across categories and channels, including seasonality, promotional lift, new product introduction, and clearance behaviour.
Model service-level and working-capital implications of proposed planning decisions.
Develop store-level sales reporting and analysis, including sell-through, location productivity, category mix, and performance against plan.
Analyze store inventory and availability, quantifying lost sales opportunity caused by stockouts or thin coverage.
Develop and refine store replenishment logic, including model stock, reorder triggers, and allocation rules across locations.
Support allocation decisions for new products, seasonal ranges, and high-demand SKUs across the store network.
Compare demand signals and inventory productivity across store and e-commerce channels to inform inventory positioning.
Establish recurring store performance and replenishment reporting and evolve it into a standard operating process.
Support inventory strategy analysis for finished jewelry and, as the team’s scope expands, component and factory inventory.
Analyze inventory aging, coverage, excess and obsolescence risk, and availability gaps, and recommend corrective action.
Contribute to assortment and SKU rationalization analysis, quantifying sales and working-capital impacts of range changes.
Assess fulfilment method performance across asset, vendor-stocked, and just-in-time models and identify opportunities to improve availability and speed.
Identify manual effort and friction in planning processes and propose data-backed improvements.
Scope and drive planning improvement initiatives, including reporting, forecasting and replenishment methodology, and tooling improvements.
Automate recurring analysis and reporting in Python to reduce manual planning effort.
Document and standardize planning processes, definitions, and calculations.
Translate complex analysis into clear recommendations for technical and non-technical audiences.
Partner with Merchandising, Finance, Operations, and Technology to align data definitions and business context.
Present findings and recommendations clearly to senior stakeholders.