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AI is the new electricity. Millions of AI engineers are needed to transform industries with AI, particularly in the realm of GenAI, and we’re building an education platform to train them. With a mission to grow and connect the global AI community, DeepLearning.AI is an education technology company that is empowering the global workforce to build an AI-powered future through world-class education, hands-on training, and a collaborative community. We’re a small tech company with serious credentials, exciting marketing challenges, and wonderful teammates.
We have a lot of data - across our learning platform, Stripe, HubSpot, PostHog, customer.io, website analytics, NPS surveys, and more - and strong engineering capacity. We know the broad shape of the questions we want to answer, spanning topics such as growth, retention, monetization, and product engagement. We’re now looking to build a solid analytical layer - someone to translate our business questions into the right metrics, dig into the data to actually answer them, and - as a byproduct - leave behind a clean, durable data foundation that gets stronger with each cycle.
We are looking for a short-term consultant to work directly with our COO and business units leaders on the above, be able to generate value in week one and build the architecture iteratively against real questions.
What you'll do:
You'll be partnering and working closely with our COO and our data team from day one - not handing off specs for the team to build but rather doing real hands-on collaborative work (SQL, dbt, Python, whatever the right tool is).
The model we have in mind:
First week: lightweight orientation on existing pipelines, warehouse, and Metabase setup. Stakeholder conversations with leaders across Product, Marketing and other teams. Align on the first ~10 most important metrics and questions to tackle.
2nd week onwards: work through that first batch - define the metrics precisely, perform EDA to answer the questions (including the why behind them and any obviously related questions), ship dashboards or analyses that stakeholders can use, and as you go, build out the data models, naming conventions, and pipeline pieces needed to support those metrics durably. Then move to another batch of ~10, then another, and another, …
With each cycle, we’ll uncover real answers to real business questions, and gain incremental, well-designed foundations that the next cycle can build on.
Deliverables:
We need quick value generated, not beautiful summary decks and long documents.
Answered business questions with the reasoning behind them, in whatever format makes them usable.
Working data models, transformations, and naming conventions that support those answers durably and that the next cycle can build on (as a byproduct of answering business questions iteratively).
A lightweight running, living view of what's been built and what's coming next - only as needed for us to always know where we are.
Who we're looking for:
3+ years of experience as a data scientist with a strong analytical instinct, ability to translate ambiguous business questions into well-defined metrics and the judgment to know which questions are actually worth answering.
AI-native builder, leveraging latest tools and AI-assisted coding to dramatically accelerate productivity. Hands-on with SQL and Python, and comfortable doing real EDA.
Enough engineering chops to collaborate with our data eng team and make sound calls about data modeling, naming, and transformation layer design.
Comfortable asking questions, making suggestions and pushing back in executive meetings.
A practical bias - natural tendency to close projects and answer questions iteratively as opposed to designing long and multi-step projects.
Bonus: familiarity with the platforms and data sources we use (Stripe, HubSpot, PostHog, customer.io, Google Analytics).
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