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# Ads AI Analytics Lead II

Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move.

[Apply for this job](#job-application)[View company](https://jobicy.com/company/instacart.md)Share30 Aug 2026Published31Listing views3Application actions29 Sep 2026Apply before  Opportunity details

## About this role.

AI SummaryInstacart is seeking an Ads AI Analytics Lead II to build production-grade AI agents that analyze advertising campaigns, diagnose performance, and recommend actions to improve ROAS, pacing, and partner outcomes. The role owns the full analytics and agent stack, including ad ontologies, dbt models, Snowflake or BigQuery data marts, Airflow pipelines, retrieval-ready datasets, RAG workflows, evaluation systems, and user-facing tooling. This person will run experiments and track advertising and AI-quality KPIs such as ROAS lift, forecast MAPE, precision/recall, latency, cost, and hallucination rate. The position partners closely with Ads Product, Engineering, Sales, Data Science, and GTM teams, with direct impact on tools used by approximately 265 sellers. It is a remote Canadian role limited to Ontario, Alberta, British Columbia, and Nova Scotia.

## Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

### Job Complexity

5/5EasyHard

### Pace & Pressure

5/5RelaxedFast-paced

### Autonomy Level

5/5GuidedFull ownership

### Communication Load

5/5IndependentCollaborative

AI insightThis is a senior, multidisciplinary applied-AI analytics role requiring strong data engineering, experimentation, advertising-domain expertise, and end-to-end agent delivery. The lead is expected to own a high-impact workstream independently while aligning many technical and commercial stakeholders.

## Salary analysis

Estimated compensation compared with the broader CA market for similar roles.

Estimated job medianBelow marketC$144,000CA market rangeC$145k–C$190k0C$209k

AI insightThe disclosed Canadian base-pay range is CAD 140,000–148,000 per year, with a midpoint of CAD 144,000. For comparison, the estimated US market base-salary range for a senior ads analytics/applied AI lead is approximately USD 145,000–190,000 annually; this market comparison is an estimate and excludes equity, refresh grants, and benefits.

## Core skills

Skills and capabilities most closely associated with this opportunity.

[Python](https://jobicy.com/jobs?search_keywords=Python.md)[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[Analytics Engineering](https://jobicy.com/jobs?search_keywords=Analytics%20Engineering.md)[Applied AI](https://jobicy.com/jobs?search_keywords=Applied%20AI.md)[Advertising Analytics](https://jobicy.com/jobs?search_keywords=Advertising%20Analytics.md)[dbt](https://jobicy.com/jobs?search_keywords=dbt.md)[Snowflake](https://jobicy.com/jobs?search_keywords=Snowflake.md)[Airflow](https://jobicy.com/jobs?search_keywords=Airflow.md)[RAG](https://jobicy.com/jobs?search_keywords=RAG.md)[Experimentation](https://jobicy.com/jobs?search_keywords=Experimentation.md)

Sample interview questionsHow would you design a trustworthy agent that diagnoses an underperforming advertising campaign?I would first define a canonical Ads ontology covering campaign objectives, budgets, bids, audiences, creatives, placements, and core metrics. The agent would use governed data marts and retrieval-grounded policy and product documentation, then apply deterministic metric checks before generating recommendations. I would require evidence citations, confidence scoring, guardrails for sensitive actions, and human approval for recommendations that change budgets or bids.

What data-quality practices would you use for dbt models powering Ads agents?

I would establish explicit data contracts, source freshness checks, schema and relationship tests, metric reconciliation, and alerting tied to SLOs. Models would be documented by business definition and ownership, with monitored transformations in Airflow. For agent-critical datasets, I would also validate semantic consistency, late-arriving data behavior, and changes that could affect downstream recommendations.

How would you evaluate a RAG workflow for Ads analytics use cases?

I would build a representative evaluation set of real Ads questions, expected evidence, and approved answer criteria. Offline measures would include retrieval recall, ranking quality, citation accuracy, answer precision, calibration, hallucination rate, latency, and cost. I would then use controlled online experiments to assess task completion, time-to-insight, recommendation adoption, and incremental business outcomes.

Describe how you would measure whether an AI recommendation system improves ROAS.

I would define the decision unit, eligible population, treatment behavior, and primary and guardrail metrics before launch. Where feasible, I would run a randomized experiment comparing users or campaigns receiving AI recommendations with a control group, measuring incremental ROAS while controlling for spend, seasonality, campaign objective, and advertiser mix. If randomization is constrained, I would use quasi-experimental methods such as matched controls or difference-in-differences and report uncertainty clearly.

How do you balance speed, cost, and safety when deploying production AI agents?

I use tiered workflows: deterministic queries and rules for straightforward cases, retrieval and smaller models for routine analysis, and more capable models only for complex reasoning. I set latency and cost budgets, monitor usage and output quality, and implement role-based access, content filtering, audit logs, and approval gates for consequential actions. Continuous evaluation and incident feedback are essential to adjusting those tradeoffs over time.

We’re transforming the grocery industry

At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers.

Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table.

Instacart is a Flex First team

There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. [Learn more about our flexible approach to where we work.](https://www.instacart.careers/flex-first)

Overview

Instacart’s Commercial Scaled Intelligence (CSI) team is an AI-first group focused on turning data into action—building products that drive revenue growth, operational efficiency, and better outcomes for our customers, shoppers, retailers, brands, and partners.

As the Ads AI Analytics Lead, you will own the intelligence behind our Ads agents. You’ll design the semantic and context layer for advertising, and build production-grade agents that analyze campaigns, diagnose performance, and recommend actions that improve ROAS, pacing, and partner outcomes. You will collaborate closely with Ads GTM, Product, Data Science, and Engineering to ship vertical agents with measurable lift.

This seat is uniquely high-impact: the tools you build are used daily by Sales, Brand Partnerships, and Ads teams, including a sales tool used by approximately 265 sellers. You will see your work show up in real workflows and on the Ads P&L. AI is the default on this team, not the experiment—and you’ll own a workstream end to end, from data models and pipelines to agent logic and the user interface. If you thrive in fast-paced environments, enjoy partnering cross-functionally, and want meaningful ownership, you’ll feel at home here.

This role is remote-friendly. Preference for candidates who can collaborate with teams based in New York City.

About the Job

* Define Ads ontologies and canonical metrics across campaigns, budgets, bids, creatives, audiences, and placements to power consistent reasoning and recommendations.
* Build robust dbt models and curated marts in Snowflake (or BigQuery) with clear data contracts, tests, SLOs, and monitoring; orchestrate pipelines with Airflow.
* Ingest, structure, and enrich unstructured Ads content; publish retrieval-ready datasets leveraging managed search/vector services to enable high-quality grounding.
* Design and evaluate RAG workflows (hybrid search, re-ranking) with explicit quality and latency targets; iterate via offline/online experiments to improve performance.
* Design agent reasoning, tools, and policies for Ads use cases, including human-in-the-loop approvals and guardrails that balance safety, cost, and speed.
* Establish evaluation suites and dashboards tracking precision/recall, calibration, hallucination rate, latency, and cost; drive continuous model and system improvements.
* Run A/B and uplift experiments to quantify business impact; own KPIs such as ROAS lift, pacing accuracy, RCA precision/recall, forecast MAPE, and time-to-insight.
* Partner with Ads Product, Ads Engineering, Sales leadership, Data Engineering, and R&D to align roadmaps, de-risk launches, and ship high-quality production agents.
* Own your workstream end to end—from data and pipelines to the UI and agent logic—delivering secure, observable, and reliable tooling that’s adopted by the field.

About You

Minimum Qualifications

* 4–7 years of experience in analytics engineering, data science, or applied AI, with advanced proficiency in Python and SQL.
* 2+ years working with ads, retail, or e‑commerce data.
* Hands-on experience with dbt and Snowflake or BigQuery, including data modeling, testing, documentation, and managing data contracts.
* Experience orchestrating data pipelines with Airflow (or a similar scheduler), including alerting and on-call support for data SLOs.
* Ability to design and run offline/online evaluations and A/B or uplift tests; familiarity with experiment design and statistical inference.
* Fluency in Ads analytics concepts: ROAS, CPA, CTR, CVR, LTV, pacing, auction dynamics, and incrementality.
* Shipped at least one production data or AI system used by business stakeholders, with demonstrable business impact.
* Experience with evaluation and guardrail frameworks and human‑in‑the‑loop QA workflows.
* Proficiency with at least one BI/visualization tool (e.g., Looker, Tableau, Mode, or Power BI).
* Bachelor’s degree in a quantitative field (e.g., Computer Science, Engineering, Statistics) or equivalent practical experience.

Preferred Qualifications

* Experience building AI-driven products or agents end to end, including retrieval design (hybrid search, re-ranking) and vector search.
* Deep expertise in advertising products and operations, with a track record of driving automation that improved ROAS, pacing, or operational efficiency.
* Applied experience with Ads modeling techniques such as forecasting, anomaly detection, uplift modeling, and causal inference.
* Hands-on experience with workflow automation or internal tooling (e.g., Retool, Superblocks, Zapier, n8n, Gumloop) and/or front-end frameworks (e.g., React) to translate insights into actions.
* Familiarity with retail media and ad ecosystems (e.g., Amazon Ads, Google Ads, Meta, Shopify, DoorDash) and their measurement frameworks.

#LI-Remote

Instacart provides highly market-competitive compensation and benefits in each location where our employees work. This role is remote and the base pay range for a successful candidate is dependent on their permanent work location. Please review our Flex First remote work policy [here](https://instacart.careers/flex-first/). Currently, we are only hiring in the following provinces: Ontario, Alberta, British Columbia, and Nova Scotia.

Offers may vary based on many factors, such as candidate experience and skills required for the role. Additionally, this role is eligible for a new hire equity grant as well as annual refresh grants. Please read more about our benefits offerings [here](https://instacart.careers/taste-of-instacart/).

For Canadian based candidates, the base pay ranges for a successful candidate are listed below.

CAN

$140,000—$148,000 CAD

Show more

[Apply now >](https://jobicy.com/jobs/152090-ads-ai-analytics-lead-ii.md)

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