Operations Analytics Manager
Operations Analytics Manager building and owning the end-to-end analytics stack for demand forecasting, S&OP, supply chain optimization, and costed BOM analytics. Requires 5+ years in data science or operations analytics, Python/SQL proficiency, forecasting model experience, and cross-functional partnership with leadership.
About the job
What you'll do
Operational data warehouse and infrastructure
- Design and build the operational data warehouse: integrating data from ERP (NetSuite), CRM (Salesforce), and other business systems into a unified, reliable analytics layer.
- Build and own ETL pipelines, data quality, and governance in close partnership with Business Systems on architecture and system integrations.
Demand forecasting and S&OP
- Build and own statistical and ML-driven demand forecasting models that feed the monthly S&OP cycle and inform capacity, procurement, and production planning.
- Partner with Sales, Finance, and Operations leadership to align forecast inputs and provide real-time visibility into demand signals and supply constraints.
- Build the Operations reporting suite: automated dashboards and KPI tracking for executive visibility.
Central planning and supply chain analytics
- Design and maintain analytical models for inventory optimization, lead time analysis, supplier performance tracking, and capacity utilization.
- Build predictive models to anticipate supply disruptions, demand variability, and cost escalation.
Costed BOMs and margin analytics
- Own the data pipeline and reporting layer for costed bills of materials (BOMs) ensuring visibility into material costs, labor, and overhead across product lines.
- Develop scenario models to evaluate the cost impact of design changes, sourcing decisions, and volume shifts.
Minimum Qualifications
- 5+ years in data science, analytics engineering, or operations/revenue analytics.
- Proficiency in Python and SQL; experience with cloud data platforms (Snowflake or equivalent).
- Hands-on experience building forecasting models and data pipelines in a production environment.
- Ability to translate complex model outputs into clear narratives for non-technical stakeholders and senior leadership.
- Experience working cross-functionally with Finance, Sales, Supply Chain, or Operations.
- Bachelor’s degree in supply chain, engineering, business, or related field.
Preferred Qualifications
- Experience with supply chain analytics, S&OP, costed BOMs, or manufacturing/operations environments.
- Familiarity with ERP or WMS data models (NetSuite, SAP, or similar).
- Generative AI or LLM integration experience applied to operational or analytical workflows.
Skills
Python, SQL, Snowflake, ETL, Demand Forecasting, S&Op, Supply Chain Analytics, NetSuite, Salesforce, Generative AI, LLMs
Similar jobs
Data Analytics jobs
Dropbox
United StatesAnalyzes Talent Acquisition and workforce data to identify trends, improve hiring effectiveness, and inform People and business-leader decisions. The role requires 3+ years of HR-focused analytics experience, SQL and advanced Excel proficiency, and strong stakeholder communication.
Current
New York, NY
Analyzes marketing spend, channel performance, attribution, and customer growth through reporting, experimentation, data modeling, and predictive analytics. Requires 3+ years of data analysis experience, marketing expertise, SQL, BI tools, Python, and a quantitative bachelor’s degree.
OnePay
United States
Own credit and product analytics for consumer liquidity products, shaping underwriting strategy, portfolio risk policies, and new product launches. The role requires 4–8 years of credit analytics experience, advanced SQL, AI capabilities, and strong quantitative problem-solving skills.
Square
United States
Owns payment reconciliation and financial reporting workstreams, building SQL-based controls, dashboards, metrics, and automated solutions across card networks, banks, processors, and internal ledgers. Requires 5+ years of relevant experience, expert SQL, accounting knowledge, and strong stakeholder-management skills.
Spade
New York, NY
Analyzes transaction enrichment performance, investigates data-quality anomalies, and builds dashboards and monitoring to improve product accuracy and reliability. Requires 3–5 years of analytical experience, strong SQL and Python, and experience with BI and automated alerting.