Sr Analyst, Supply Chain Planning

Blue Nile•New York, NY

6d•$75,000 - $90,000

About The Position

The Analyst, Supply Chain Planning provides the analytical foundation for demand, supply, and inventory decisions and drives initiatives to improve planning capability. Working alongside demand and supply planners, the role owns analysis, diagnostics, and reporting that support sharper and faster planning decisions. It builds and automates recurring performance reporting, investigates the causes of variance, and translates complex datasets into recommendations for leadership. The role offers exposure to forecasting, replenishment, and inventory strategy, with potential to grow into planning or analytics leadership as the team expands. An initial priority is to develop store sales performance and replenishment analytics as Blue Nile’s retail showroom footprint grows alongside e-commerce. The role establishes visibility into location-level sales and product availability, then uses the analysis to develop repeatable approaches to inventory allocation and replenishment. The planning organization uses a Python-native analytics stack rather than manual spreadsheets, and expects team members to work fluently with SQL, Python, and business intelligence tools. The position supports planning analytics and reporting, demand and supply planning, store performance and replenishment, inventory and assortment analysis, and continuous improvement and automation. It works cross-functionally with Merchandising, Finance, Operations, and Technology and presents findings to senior stakeholders. Blue Nile is part of Signet Jewelers, a purpose-driven company whose mission is to enable all people to Celebrate Life and Express Love. Signet Jewelers is Great Place to Work-Certified. Annual base pay is $75,000–$90,000; the final rate depends on experience and qualifications. Blue Nile will not sponsor a new applicant for employment authorization for this position. The company is an equal opportunity employer.

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 is expected in this code-forward planning organization.
Analytical rigor and attention to detail, including reconciling data before reporting.
Intellectual curiosity and ability to pursue root causes rather than stop at the first plausible explanation.
Clear communication and ability to translate technical analysis into actionable business recommendations.
Business acumen and ability to connect operational metrics to commercial and financial outcomes.
Ownership mindset and initiative to improve processes.
Process orientation and continuous improvement mindset.
Comfort with ambiguity and ability to prioritize competing demands in a fast-paced environment.
Ability to collaborate effectively across planning, commercial, and technology teams.

Nice To Haves

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

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 create trustworthy, decision-ready views.
Define and track planning KPIs against targets and develop exception reporting to surface risks early.
Investigate variance to root cause and explain its drivers, distinguishing demand signals from data quality issues, seasonality, and one-off effects.
Prepare analytical inputs and supporting materials for weekly planning and monthly IBP cycles.
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 when statistical output may require human override.
Analyze demand patterns across categories and channels, including seasonality, promotional lift, new product introductions, and clearance behavior.
Model the service-level and working-capital implications of proposed planning decisions.
Build analysis and reporting for store-level sales performance, including sell-through, productivity by location, category mix, and performance against plan.
Analyze store inventory and availability, quantifying lost sales opportunities related to stockouts or thin coverage.
Develop and refine store replenishment logic, including model stock, reorder triggers, and allocation rules.
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 and evolve recurring store performance and replenishment reporting into a standard operating process.
Analyze inventory strategy 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; recommend corrective actions.
Contribute to assortment and SKU rationalization analysis, quantifying sales and working-capital impacts of range changes.
Assess fulfilment performance across asset, vendor-stocked, and just-in-time models and identify opportunities to improve availability and speed.
Identify planning-process friction and manual effort and propose data-backed improvements.
Scope and drive planning improvements, including reporting streamlining and enhancements to forecasting, replenishment methodology, and tooling.
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, concise recommendations for technical and non-technical audiences.
Partner with Merchandising, Finance, Operations, and Technology to align on data definitions and business context.
Present findings and recommendations clearly to senior stakeholders, with appropriate confidence in the underlying data.