Manager, Forecasting & Quantitative Analytics (Supply Chain Forecasting Lead)

Duration: Long term Contract

Work mode: Remote

Job Purpose

• The Manager, Forecasting & Quantitative Analytics is the working lead for a small team responsible for insurance-driven demand forecasting, predictive analytics, inventory financial-risk models, and high-value analytical decision support. The role is accountable for the completeness, technical soundness, reproducibility, and operational usefulness of all team deliverables.
• This is a player-coach role, not primarily an administrative management position. The Manager personally builds and reviews models, writes Python and SQL, challenges assumptions, and resolves the team's most difficult technical or business problems while coaching three specialist individual contributors: the Principal Actuary, Data Scientist, and Senior Data Analyst.

Primary Job Accountabilities / Responsibilities

Forecasting quality assurance and technical review | 30%

• Own the team's work-product standards and release process across demand forecasts, model-family projections, valuation studies, scenarios, contract analyses, dashboards, and ad hoc results.
• Personally review material methodology choices, source-data reconciliation, backtests, uncertainty ranges, reasonableness, edge cases, and narratives; require traceability from source to business decision.
• Challenge and synthesize specialist outputs while respecting the Principal Actuary's ownership of actuarial methods and Finance/Accounting decision rights.

Hands-on analytics and model development | 25%

• Develop, debug, benchmark, and improve forecasts, probabilistic models, Python scripts, complex SQL analyses, and operational scenario tools alongside team members.
• Investigate claim patterns, device migration, repair supply, inventory positions, and planning exceptions; lead technically demanding work when complexity or urgency requires.

Demand planning integration and stakeholder delivery | 20%

• Set the forecasting calendar, analytical priorities, milestones, and delivery expectations for Supply Chain, Finance, Operations, Account Teams, and Actuarial stakeholders.
• Ensure forecasts translate appropriately into claims, replacement devices, repairs, inventory-risk exposures, and purchase/disposition decision support.
• Own communication of assumptions, ranges, methodological changes, exceptions, and decisions requiring leadership action.

Data and model governance | 15%

• Implement version control, repeatable data pipelines, model documentation, benchmark comparisons, drift monitoring, and issue management for team work products.
• Partner with data platform teams to strengthen semantic definitions, data lineage, controls, and production readiness across policy, claims, repair, inventory, procurement, and resale data.

Focused people leadership and capability growth | 10%

• Direct and develop a small team of approximately three specialist individual contributors; set goals, coach performance, prioritize capacity, and remove blockers.
• Create a learning-oriented technical review culture while continuing to own substantial individual technical work.

Management Responsibility

• Direct people management of an anticipated three-person technical team: Principal Actuary, Data Scientist, and Senior Data Analyst. Responsible for performance, coaching, workload, and quality management; remains a substantial hands-on technical contributor. No planned additional management layers.

Financial Responsibility

• Owns analytical quality and recommendations supporting material forecast, working capital, inventory-risk, and supplier decisions, but not purchasing execution, statutory/accounting reserve sign-off, or trade authorization.

Required Experience, Skills, and Knowledge

• Bachelor's degree in statistics, mathematics, actuarial science, computer science, operations research, economics, engineering, or related quantitative discipline, or equivalent relevant experience.
• 8+ years of applied forecasting, advanced analytics, data science, actuarial analytics, supply chain analytics, or quantitative risk experience, including experience coordinating or mentoring technical professionals.
• Hands-on expertise in Python and advanced SQL, with recent evidence of personally building, troubleshooting, and improving production-grade models or analytical workflows.
• Strong command of predictive modeling, time-series forecasting, cohort analysis, uncertainty quantification, rolling backtests, error decomposition, and model-validation practices.
• Ability to critically assess statistical, actuarial, financial-risk, and data-analytical work for methodological integrity, reproducibility, completeness, and business relevance.
• Experience converting analytical outcomes into operational recommendations with clear tradeoffs and stakeholder accountability.
• Demonstrated ability to lead a small team while retaining meaningful personal ownership of technical work.

Preferred Experience, Skills, and Knowledge

• Insurance claims/exposure modeling, reverse logistics, used-device markets, inventory planning, or rapidly depreciating-asset economics.
• Experience with physically settled procurement commitments, inventory risk, optimization, or contract economics.
• Experience building quality controls and review cadences across cross-disciplinary actuarial and data-science teams.
• Experience in Snowflake or another enterprise data warehouse, modern Python analytical libraries, Git, and production orchestration tools.

Measures of Success

• On-time delivery of trustworthy forecasts and analyses; independently verified work-product quality; appropriate probabilistic calibration; reduced repeat analyses and defects; cross-functional adoption; employee development and meaningful direct technical output.

Work Environment / Travel

• Professional office or approved remote/hybrid environment; routine computer-based analytical work. Occasional travel for operations, partner meetings, or business reviews. Location and travel expectations are subject to confirmation.