Ai Scientist Salary In Jobs (NOW HIRING)

Order.co is the System of Action for the Office of the CFO, transforming the way businesses purchase and pay into an intuitive, B2C-like shopping experience. Order.co leverages embedded AI agents and embedded financial products to reinvent the way businesses connect with their vendors.

End users enjoy a seamless, zero-training buying experience, while finance and procurement leaders gain a single platform to orchestrate how the business “should operate”. The result is an all-in-one solution that serves as a gravitational pull for spend and data, automating and eliminating procurement and finance workflows from requisition to reconciliation along the way.

Order.co is on the cutting edge of B2B Agentic Commerce, poised to be the market leader in creating a more predictive, prescriptive, and personalized experience for users.

Founded in 2016 and headquartered in New York City, Order.co oversees nearly half a billion in annualized spend across hundreds of customers like WeWork, SoulCycle, Lume, and [solidcore] . Order.co has raised $75M in funding from industry-leading investors like MIT, Stage 2 Capital, Rally Ventures, 645 Ventures, and more. Order.co has been proudly named a 50 to Watch by Spend Matters and a Best Place to Work by BuiltIn and Inc. Magazine.

We are hiring an AI Scientist or Senior AI Scientist to ship applied AI from problem definition through deployed production, with direct accountability for measurable business outcomes.

This is an embedded role on a product engineering squad building customer-facing ordering and workflow capabilities. Final title and scope are set based on the experience and impact demonstrated in our interview process.

Success at either level means iterative, low-cycle-time delivery that compounds into meaningful KPI movement. Scope grows through the magnitude and reach of impact, not through long delivery windows.

Dimension

AI Scientist

Senior AI Scientist

Scope

Owns a portfolio of AI opportunities across teams; sets prioritization, not only execution

Level of Impact

Measurable KPI movement on assigned initiatives Company-priority KPI movement with cross-team reach

Technical leadership

Leads initiative design, rollout, failure-mode handling, and model-operations playbooks for owned systems Leads architecture and delivery patterns others adopt; raises team-wide model-to-production standards Mentors junior colleagues; improves standards within initiative scope Mentors experienced ICs; shapes cross-team technical direction

Stakeholder reach

Strong influence within embedded squad and data partners Aligns senior stakeholders across product, engineering, and operations on AI bets

Experience signal

5–7+ years in applied data science / ML with repeated production delivery 8+ years with portfolio-level outcome ownership

How to read this: If your strongest proof is initiative-level execution with production impact and hands‑on delivery, AI Scientist may fit. If you have repeatedly owned portfolio-level AI bets across teams—with prioritization authority and standards others follow— Senior AI Scientist may fit.

Near-term focus areas include:

• Predictive ordering — ML and AI capabilities that improve how customers plan and place orders
• Agentic copilots for workflow management — intelligent assistance embedded in core product workflows (technical direction weighted toward Senior AI Scientist hires)

You will be embedded day-to-day with a product/engineering squad while reporting into the data team.

• Lead end-to-end lifecycle execution: problem framing, experimentation, model/system design, production rollout, and post-launch optimization that incorporate HITL feedback.
• Be accountable for business outcomes (for example conversion, margin, operational efficiency, retention)—not model metrics alone.
• Translate ambiguous business goals into clear technical bets, delivery plans, and measurable success criteria.
• Ship iteratively with short feedback loops; deliver meaningful impact at each step.

• Own one or more high-leverage initiatives per quarter with clear KPI hypotheses and delivery accountability.
• Balance model quality, operational constraints, and time-to-value on assigned bets.
• Define rollout strategy, failure modes, and iterative improvement loops for systems you own.
• Establish model-operations playbooks for incident response and performance degradation on owned systems.
• Mentor junior scientists and influence technical standards within your initiative scope.

• Own a portfolio of AI opportunities tied to company-priority KPIs across multiple teams.
• Identify and prioritize highest-leverage opportunities; build the execution path, not only execute assigned work.
• Lead architecture and operational patterns for scalable model delivery that others can reuse.
• Raise team standards through repeatable model-to-production patterns, implementation quality, and decision velocity.
• Mentor experienced ICs and align senior stakeholders on AI prioritization and sequencing.

• Identify high-leverage AI opportunities using business context, data diagnostics, and technical feasibility.
• Design practical AI/ML solutions (leveraging both deterministic and LLM/agent-based patterns where appropriate) with clear trade-offs on accuracy, latency, cost, and reliability.
• Build and productionize complex model systems with engineering-quality discipline: testing, observability, rollback/fallback strategy, human-in-the-loop integration, and incident readiness.
• Define evaluation frameworks that connect offline/online model quality to KPI impact and risk/accuracy controls.
• Partner closely with product, engineering, analytics, and operations to align scope, sequencing, and accountability.

• Drive hands‑on delivery on predictive ordering capabilities from early production through optimization.
• Work closely with a principal-level data scientist on architecture choices while owning execution velocity.

• Set technical direction for agentic workflow / copilot capabilities in partnership with product and engineering leadership.
• Co‑own prioritization and standards with product, engineering, and data leadership — not execution alone.
• Mentor scientists and technical peers on applied AI execution, production quality, and pragmatic delivery.

• Proven track record delivering AI/ML systems to production with measurable business outcomes.
• Deep familiarity with current LLM and agent technologies, including practical evaluation and failure‑mode handling.
• Demonstrated ability to productionize complex models and model‑adjacent systems with strong reliability and observability practices.
• Heavy, day‑to‑day use of AI‑native engineering workflows (coding, framing/design, debugging, and code review) for at least the past 18 months.
• Working implementation proficiency across at least two technical ecosystems/cloud stacks (for example AWS and GCP).
• Strong quantitative foundation in experimentation, statistical reasoning, and model evaluation.
• Strong collaboration skills; can drive alignment and decisions under ambiguity.

• 5–7+ years in applied data science / machine learning roles with repeated production delivery.
• Track record owning initiatives end‑to‑end—not only contributing to models owned by others.
• Leadership‑level influence within a cross‑functional squad; improves team decision quality through technical rigor.

• 8+ years in applied data science / machine learning roles with portfolio‑level outcome ownership.
• Track record owning AI/ML initiatives from concept through production and measurable business impact at cross‑team scope.
• Stakeholder leadership across product, data, engineering, and operations; can resolve prioritization under ambiguity.

• Experience implementing local/self‑hosted AI solutions (for example self‑managed agent infrastructure on‑prem or in your own environment).
• Experience with retrieval systems, vector search, ranking/recommendation, or other production AI personalization workflows.
• Experience in e‑commerce, B2B vendor management, financial products, or external systems integrations.

• Experience setting team‑level standards for model governance, monitoring, and responsible AI practices.
• Experience mentoring senior ICs and shaping cross‑team technical direction.

• Launches two or more AI capabilities to production on predictive ordering with clear KPI hypotheses and measurable outcome movement.
• Establishes reliable model‑operations practices (testing, observability, incident playbooks) for owned systems.
• Delivers iteratively with low cycle time; each release produces an evaluable business signal.
• Builds effective working rhythm with principal‑level data scientist partner and embedded product squad.

• Everything above, plus:
• Launches AI capabilities across more than one initiatives with measurable KPI impact at company‑priority scope.
• Establishes a repeatable, low‑cycle‑time model delivery pattern adopted by others on the team.
• Creates durable alignment across product, engineering, and data stakeholders on AI prioritization and execution.
• Defines technical trajectory for agentic workflow / copilot capabilities alongside predictive ordering.

• Embedded squad: Day‑to‑day work alongside product/engineering on customer‑facing capabilities.
• Principal‑level pairing: Close collaboration with a principal‑level scientist on architecture, prioritization, and execution—hands‑on‑keyboard from day one.
• Cross‑functional partners: Product, engineering, analytics, and operations.
• Leveling at offer: Title reflects scope and impact demonstrated in process, not tenure alone.