Senior Applied Scientist

QXO•Seattle, WA

14d

About The Position

We’re looking for bold, entrepreneurial talent ready to help build something extraordinary — and reshape the future of building products distribution. QXO is a publicly traded company founded by Brad Jacobs with the goal of building the market-leading company in the building products distribution industry. On April 30, 2025, QXO completed its first acquisition: Beacon Building Products, a leading distributor in the sector. We are building a customer-focused, tech-enabled, and innovation-driven business that will scale rapidly through accretive M&A, organic growth, and greenfield expansion. Our strategy is rooted in delivering exceptional customer experiences, improving operational efficiency, and leveraging data, digital tools, and AI to modernize a historically under-digitized industry.

Requirements

2+ years of experience in Applied Science / Data Science / Quantitative Research, with a strong record of shipping models into production.
Expertise in one or more of the following: Time series forecasting Pricing & revenue management Supply chain / inventory / logistics modeling Operations research / mathematical optimization
Proficiency in Python and scientific computing libraries (NumPy, pandas, etc.).
Solid SQL skills and experience working with modern data warehouses/lakehouses.
Hands-on experience designing experiments, analyzing results, and working with ambiguous real-world data.
Excellent communication skills and demonstrated ability to lead projects spanning multiple teams.

Nice To Haves

Prior experience in retail, B2B distribution, manufacturing, or the building materials / construction industry.
Experience with revenue management, discounting, and contract/pricing architectures.
Experience with supply chain planning systems (MRP/DRP, S&OP) and operational constraints.
Familiarity with deep learning libraries (e.g., PyTorch, TensorFlow, JAX).
Experience integrating models with downstream applications or AI agents, including considerations for latency, reliability, and interpretability.
Experience with large-scale or distributed data/compute systems and ML platforms (MLOps, feature stores, model registries, CI/CD for models).
Advanced degree (MS or PhD) in a quantitative field such as Statistics, Operations Research, Applied Mathematics, Computer Science, or a related discipline.

Responsibilities

Build and productionize time series models for demand, sales, and inventory (e.g., hierarchical forecasting, intermittent demand, seasonality/trend modeling, multivariate forecasting).
Develop approaches to handle sparse, volatile, and evolving data environments.
Develop pricing models and policies, including elasticity estimation, margin optimization, and discounting/contract structures.
Support pricing for quotes and BOMs, including guardrails, risk/margin checks, and complex B2B rules around bundling and substitution.
Design models for inventory planning, replenishment, and allocation under real-world constraints (lead times, MOQs, service levels).
Build tools to improve fill rates, reduce stockouts, and manage working capital.
Formulate and solve optimization problems (linear, mixed-integer, non-linear, heuristics/approximation) for routing, allocation, capacity, and network flows.
Integrate optimization with forecasting/pricing models to support end-to-end decisions.
Combine statistical methods, machine learning, and operations research techniques as appropriate.
Stay current on relevant research and bring practical, scalable methods into production.
Translate messy business problems into clear technical formulations and evaluate alternative approaches.
Work with engineers to turn models into robust services powering internal tools, APIs, and AI agents (e.g., quoting & BOM agents, pricing copilots, supply planning tools).
Build or contribute to simulation and scenario analysis frameworks to test policies before rollout.
Define and implement offline and online evaluation (experiments, policy evaluation, counterfactual analysis).
Partner with data engineering to ensure data quality, structure, and accessibility for modeling.
Define metrics for your domain (forecast accuracy, stockouts, margin impact, quote win rates, price realization, etc.).
Design experiments and quasi-experiments to measure the business impact of new models and policies.
Clearly communicate findings, trade-offs, and recommendations to stakeholders.
Work closely with Sales, Marketing, Supply Chain, Finance, and Product teams to understand constraints, workflows, and incentives.
Collaborate with AI Engineers who will embed your models inside intelligent agents and applications.
Mentor other scientists and engineers; elevate standards around methodology, code quality, and evaluation.
Influence roadmap and strategy by highlighting long-term modeling opportunities and risks.

Benefits

Annual performance bonus
Long term incentive (equity/stock)
401(k) with employer match
Medical, dental, and vision insurance
PTO, company holidays, and parental leave
Paid Time Off/Paid Sick Leave: Applicants can expect to accrue 15 days of paid time off during their first year (4.62 hours for every 80 hours worked) and increased accruals after five years of service.
Paid training and certifications
Legal assistance and identity protection
Pet insurance
Employee assistance program (EAP)