Product Data Specialist

Vinci is building the future of engineering intelligence.

We created the first AI-native computational platform built on Foundation Models for Physics. It lets engineering teams bring deterministic physical reasoning into every stage of semiconductor and electronics design. By combining AI, physics and high-performance computing, Vinci makes it possible to explore far more design alternatives, uncover insights that were previously out of reach, and optimize products in minutes instead of days.

Leading semiconductor and electronics companies already use Vinci to solve some of the world’s most computationally demanding engineering problems. As we grow, we’re building the team that will define the next generation of engineering infrastructure.

Engineers at some of the most advanced hardware companies in the world use Vinci every day. We are looking for someone to help us make the best product possible for these hardware companies. You will do this by being the founding data scientist at Vinci, setting the foundations of Data Science at Vinci, using data to answer questions at all scales and scopes.

The insights and answers you generate will drive what we build next, what we retire, and where the product experience needs work.

You will start by establishing how engineers use the product. From there, you will connect that behavior to accounts, contracts, and pipeline, so that usage tells us where an account is ready to expand, where it is at risk, and what a customer is trying to do that we do not yet support.

Your work will have several audiences. Go-to-market teams need to know what customers are trying to do and which accounts are ready to grow. Management needs to know what each feature is worth and what that means for product strategy. Engineers need to understand a workflow that is failing for a user, see how usage is scaling, and find where the product can be improved.

Each of these groups brings its own questions and its own decisions. You bring the evidence that will drive decisions, and you bring the questions that nobody thought to ask. A dashboard that reports a number is the starting point for this role, not the output. The output is the explanation of why the number is what it is and what we should do about it.

• Usage measurement and insights. Define what engagement means for a professional engineering tool, build the metrics that capture it, publish them where the teams that need them will see them, and keep them trustworthy as the product changes.

• Product insight. Pose and answer questions about feature adoption, workflow patterns, and friction that will inform product strategy and feature decisions. Partner with product management on the questions they bring, and originate your own.

• Engineering support. Help engineers identify and debug workflows that are failing, monitor how usage scales across customers and features, and surface where the product can be improved.

• Usage to business. Join application behavior with account and revenue data to find where usage predicts expansion, churn, or an unmet need, and turn that into recommendations the sales and product teams can act on.

• Model usefulness. Measure whether users find the model's output useful in their workflow. The AI team owns how good the model is. You own whether users are getting value from it.

• Getting the data. Build and maintain the pipelines that turn source databases and unstructured data into analysis-ready data sets while protecting customer confidentiality. You can think about high-level analysis but also translate that into and build the pipelines needed to connect the databases, do the analysis and present results to executives.

The measure of the role is how often product and go-to-market decisions at Vinci are made with evidence that you produced, and how often that evidence changed a decision.

You are not satisfied when you have answered the question you were asked. You want to know why the answer is what it is, and you keep going until you do. When a metric moves, your first instinct is to find the cause, not to annotate the chart. You have a record of analyses that changed what a team built or how a company sold, and you can explain how the finding got from your notebook into the decision.

You drive the insight from start to finish. Sometimes the work is analysis. Sometimes it is building the pipeline, constructing a data set from unstructured logs, selecting the right tool for a processing job, or…