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India
Remote only
HighLevel's AI portfolio spans more than a dozen products, from conversational and voice agents that handle customer interactions on a business's behalf to copilots that help operators run the business itself. These products are sold both as a bundled subscription and on a pay-per-use basis, across a base of more than a million sub-accounts.
As Senior Product Analyst for AI Products, you will own the measurement of that portfolio. You will run the recurring business review, maintain the metric definitions for each product, report performance against the operating plan, and analyse retention, adoption and the unit economics of AI usage. Your work will inform the AI roadmap and will feed into quarterly and board-level reviews.
The role is hands-on. You will write SQL against ClickHouse and Snowflake, build the reporting layer in Superset, and take responsibility for whether a figure is correct before anyone acts on it. You will work directly with the VP of Product and with the product managers who own each AI product, and we will expect you to form a view on what the data means and to defend it.
Run the recurring business review for the AI portfolio, from building the underlying analysis through to presenting the results to product leadership.
Define and document the metrics for each AI product, including activation, adoption, the north star metric and penetration, and keep those definitions consistent across every report.
Build and maintain the Superset datasets, charts and dashboards the AI team depends on, and audit the existing reporting for hardcoded filters, stale queries and definitions that have drifted.
Write SQL against ClickHouse and Snowflake, including against semi-structured JSON columns, to answer the questions our dashboards cannot.
Report each product's performance against the annual operating plan and subsequent re-forecasts, and explain the variances in terms of what happened in the product.
Analyse retention, cohort behaviour and adoption funnels at the sub-account level, and establish where and why accounts stop using a product.
Build the unit economics of AI usage, converting token consumption into cost by model and reporting cost-to-revenue ratio and margin by product.
Report on AI product quality, covering version-over-version evaluation results, containment and escalation rates, and the sampling design behind those figures.
Reconcile figures that disagree between sources, and decide which source should be used to answer which question.
Specify instrumentation with product managers before a feature ships, analyse in-product behaviour in Pendo once it has, and size the opportunities and experiments that go into the roadmap.
At least six years of experience in product or business analytics, including time spent working directly with product teams.
Strong SQL skills. You should be able to write multi-CTE queries, window functions and cohort logic without assistance, query semi-structured JSON in Snowflake, and move into Python or R for the work SQL cannot do.
Production experience with a cloud data warehouse, ideally ClickHouse and Snowflake.
The ability to author in Superset or Tableau, including building datasets and dashboards, and to open an existing chart definition and work out what it actually measures.
Experience with a product analytics tool such as Pendo, Amplitude or Mixpanel, including defining events and tracking plans.
Experience owning a recurring metrics pack or business review that other people depended on.
Careful attention to data grain and time period. You should check whether a figure is measured at the account or sub-account level before using it, and recognise what an incomplete period does to a cohort table.
A good command of SaaS metrics, including MRR and ARR movements, expansion and contraction, churn, retention and ARPU.
Enough statistical grounding to size and read an experiment honestly, and to say when a result is too noisy to act on.
Clear written English. A large part of this job is explaining an analysis in a short piece of writing that a busy reader can act on.
Experience analysing an AI or LLM product, particularly token consumption, model cost and quality evaluation.
Familiarity with usage-based or hybrid pricing models and the reporting problems they create.
Experience with dbt or a comparable transformation layer.
A background in CRM, agency or SMB SaaS products.
Experience with subscription analytics tools such as ChartMogul.
At HighLevel, we foster an exciting and dynamic work environment driven by a passionate team. We believe in a collective responsibility where no task is considered someone else's job. Our unwavering focus is on providing value to our users, and we achieve this by delivering solutions swiftly through lean principles, allowing us to bring products to market in weeks rather than quarters. Every good idea is put to the test, ensuring that we maintain a high standard of innovation. We prioritise the well-being of our team, recognising that by taking care of them, they can better serve our users. We embrace the concept of continuous and iterative improvement, understanding that progress is an ongoing journey. We are also a well-funded and profitable company.
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