AI Scientist / Senior AI Scientist

Role Description

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.

How expectations differ by level

• AI Scientist:
  • Owns one or more high-impact initiatives end-to-end (problem definition → production → KPI impact)
  • Measurable KPI movement on assigned initiatives
  • Leads initiative design, rollout, failure-mode handling, and model-operations playbooks for owned systems
  • Mentors junior colleagues; improves standards within initiative scope
  • Strong influence within embedded squad and data partners
  • 5–7+ years in applied data science / ML with repeated production delivery
• Senior AI Scientist:
  • Owns a portfolio of AI opportunities across teams; sets prioritization, not only execution
  • Company-priority KPI movement with cross-team reach
  • Leads architecture and delivery patterns others adopt; raises team-wide model-to-production standards
  • Mentors experienced ICs; shapes cross-team technical direction
  • Aligns senior stakeholders across product, engineering, and operations on AI bets
  • 8+ years with portfolio-level outcome ownership

About the work

• 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

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

Scope and ownership

• Shared Expectations:
  • 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.
• AI Scientist:
  • 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.
• Senior AI Scientist:
  • 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.

Core responsibilities

• Shared Responsibilities:
  • Identify high-leverage AI opportunities using business context, data diagnostics, and technical feasibility.
  • Design practical AI/ML solutions 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.
• AI Scientist:
  • 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.
• Senior AI Scientist:
  • 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.

Required qualifications

• 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.

AI Scientist Level

• 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.

Senior AI Scientist Level

• 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.

Preferred qualifications

• Experience implementing local/self-hosted AI solutions.
• 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.

Senior AI Scientist (additional)

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

What success looks like (first 6 - 9 months)

• AI Scientist:
  • Launches two or more AI capabilities to production on predictive ordering with clear KPI hypotheses and measurable outcome movement.
  • Establishes reliable model-operations practices 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.
• Senior AI Scientist:
  • Launches AI capabilities across more than one initiative 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.

Working model

• 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.