- Страна
- Канада
- Зарплата
- 140 000 CA$ – 148 000 CA$
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на вакансии с ИИ

Ads AI Analytics Lead II
Отличная вакансия в топовой технологической компании с фокусом на передовые AI-технологии. Предлагает удаленную работу, конкурентную зарплату и возможность влиять на продукт мирового уровня.
Сложность вакансии
Высокая сложность обусловлена необходимостью сочетать глубокие знания в Analytics Engineering (dbt, Snowflake) с практическим опытом в области AI (RAG, LLM-агенты) и специфической экспертизой в рекламных метриках.
Анализ зарплаты
Предложенная зарплата в 140-148 тыс. CAD соответствует рыночному уровню для Senior/Lead ролей в Канаде, однако находится ближе к нижней границе для узкоспециализированных AI-позиций в крупных тех-компаниях. Дополнительные бонусы в виде опционов (equity) могут значительно повысить общую компенсацию.
Сопроводительное письмо
I am writing to express my strong interest in the Ads AI Analytics Lead II position at Instacart. With over 6 years of experience in analytics engineering and a deep focus on retail media, I have consistently delivered data products that bridge the gap between complex machine learning models and actionable business insights. My expertise in dbt, Snowflake, and Python, combined with a solid understanding of RAG workflows and agentic reasoning, aligns perfectly with your mission to build vertical AI agents for the Ads ecosystem.
In my previous roles, I have successfully designed semantic layers and automated performance diagnostics that directly improved ROAS and pacing for large-scale ad campaigns. I am particularly drawn to Instacart's 'Flex First' culture and the CSI team's AI-first approach. I am eager to bring my experience in causal inference and uplift modeling to help optimize commercial performance and drive measurable lift for your partners.
Составьте идеальное письмо к вакансии с ИИ-агентом

Откликнитесь в instacart уже сейчас
Присоединяйтесь к Instacart и создавайте будущее ритейл-медиа с помощью передовых AI-агентов!
Описание вакансии
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.
Overview
The Commercial Scaled Intelligence (CSI) team is an AI-first team dedicated to delivering actionable commercial insights and scalable automation to drive revenue growth and operational efficiency across the company. The team focuses on intelligence generation, predictive analytics, and workflow automation to enable data-driven decision-making and optimize commercial performance.
As an Ads AI Analytics Lead II, you will own the intelligence behind our Ads agents. You will design the Ads semantic/context layer and build vertical AI agents that analyze campaigns, diagnose performance, and recommend actions that improve ROAS, pacing, and partner outcomes. You will partner with Ads GTM, Product, Data Science, and Engineering to ship production agents with measurable lift.
About the Job
- Define Ads ontologies and metrics for campaigns, budgets, bids, creatives, audiences, and placements.
- Build dbt models and curated marts in Snowflake with clear data contracts, tests, and SLOs.
- Ingest and enrich unstructured Ads content and publish retrieval‑ready datasets using our managed search/vector services.
- Design and evaluate retrieval workflows (RAG) with existing services for hybrid search and re‑ranking; set quality/latency targets and iterate via experiments.
- Design agent reasoning and policies on ads, including tool definitions and human‑in‑the‑loop approvals.
- Establish evaluation suites covering precision/recall, calibration, hallucination rate, latency, and cost.
- Run A/B or uplift experiments to quantify impact and guide iteration.
- Translate Ads problems into agent behaviors and own KPIs such as ROAS lift, pacing accuracy, RCA precision/recall, forecast MAPE, and time‑to‑insight.
About You
Minimum Qualifications
- 4–7 years in analytics engineering, data science, or applied AI with strong SQL and Python.
- 2+ years of domain expertise in ads, retail, or e-commerce data.
- Advanced Proficiency in Python and SQL, with experience using dbt and Snowflake or BigQuery, including skills in data modeling, testing, and managing data contracts.
- Deep Expertise in orchestrating data pipelines using dbt and Airflow
- Experience with at least one data visualization tool (Tableau, Mode, Power BI, Looker, or similar)
- Ability to design offline/online evaluations and run A/B or uplift tests
- Fluency in Ads analytics concepts such as ROAS, CPA, CTR, CVR, LTV, pacing, auction dynamics, and incrementality.
- Strong stakeholder communication with a track record of shipping production data or AI systems that drove business impact.
- Understanding of ML models to drive recommendations on bid, keywords, and budgets
- Experience with evaluation and guardrail frameworks and human‑in‑the‑loop QA.
Preferred Qualifications
- Strong understanding of AI and machine learning concepts, with experience creating AI-driven products.
- Deep expertise in advertising products, including leading and driving automation projects.
- Proven ability to improve operational efficiency through automation initiatives in fast-paced environments.
- Applied experience in modeling techniques for Ads, including forecasting, anomaly detection, uplift modeling, and causal inference.
- Hands-on experience with workflow automation and low-code development platforms (Zapier, n8n, Gumloop, Superblocks)
- Familiarity with retail media or ad platforms, including Amazon, Google, Meta, Shopify, or DoorDash.
#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. 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.
For Canadian based candidates, the base pay ranges for a successful candidate are listed below.
CAN
$140,000—$148,000 CAD
Создайте идеальное резюме с помощью ИИ-агента

Навыки
- SQL
- Python
- dbt
- Snowflake
- Airflow
- RAG
- Machine Learning
- A/B Testing
- Data Modeling
- Tableau
- Looker
- BigQuery
- Causal Inference
- Uplift Modeling
Возможные вопросы на собеседовании
Проверка технического владения стеком и понимания качества данных.
Как бы вы спроектировали семантический слой в Snowflake с использованием dbt для поддержки AI-агента, которому требуется доступ к метрикам ROAS и темпам расходования бюджета в реальном времени?
Оценка опыта работы с современными AI-технологиями.
Опишите ваш опыт настройки RAG-систем: как вы подходите к оценке качества поиска (retrieval) и минимизации галлюцинаций в ответах агента?
Проверка бизнес-ориентированности и знания домена Ads.
Если наш AI-агент рекомендует изменить ставку, какие метрики и методы (например, uplift-моделирование) вы бы использовали для проверки эффективности этой рекомендации?
Оценка навыков работы со стейкхолдерами и процессами.
Расскажите о случае, когда вам приходилось внедрять систему 'human-in-the-loop'. Как вы балансировали между автоматизацией и необходимостью ручного одобрения?
Проверка навыков решения проблем и аналитического мышления.
Как вы будете подходить к диагностике причин (RCA), если AI-агент выдает неверный прогноз по MAPE для рекламной кампании?
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- Страна
- Канада
- Зарплата
- 140 000 CA$ – 148 000 CA$