About BlueAlpha

BlueAlpha uses econometrics and causal inference to help growth organizations understand what drives revenue, then operationalize those decisions with AI. We’re already working with large enterprises to bring these capabilities into their growth operations.

Our vision is to use the causal graphs generated through this work to train our own specialized small language models for growth decision-making. Each customer engagement helps us understand which decisions these models need to support and how to build them into products that growth teams use every day.

You’ll join a founding team that includes former Tesla engineers and scientists, alongside founders who have raised over $100 million and successfully exited companies. You’ll work closely with this team to turn our data science capabilities into products, from identifying the right customer problem to shaping the experience and getting it into users’ hands.

We’re looking for someone with a data science background who wants to move into product ownership. If you find yourself asking how someone understands an analysis, trusts it, and uses it to make a better decision, this role puts you in charge of answering those questions.

The role

You’ll own the product direction and delivery of capabilities that bring data science into our customers’ workflows, from interpreting measurement results to planning actions and automating recurring decisions.

Working closely with customers, Data Science, Engineering, and Customer Impact, you’ll identify valuable problems, prototype solutions, prioritize what to build, and follow through from early exploration to launch and adoption.

Your technical background will help you understand what our models can support, where uncertainty matters, and what needs further validation. Your product judgment will determine how those capabilities become clear, trustworthy, and useful experiences.

This is a hands-on product role. You’ll work with data, test ideas, and build prototypes as part of discovery. You’ll partner with Data Science on methodology and validation, and with Engineering on production implementation.

Previous product management experience is welcome but isn’t required. We’re interested in evidence that you can connect technical work to user needs and take responsibility for the outcome.

What you’ll do

Understand the decisions customers need to make. Spend time with growth teams to understand their goals, workflows, and constraints. Identify where better measurement, recommendations, or automation would materially improve a decision.

Own a product area and its priorities. Define the problems we should solve, the outcomes we’re aiming for, and the order in which we should tackle them. Make tradeoffs across customer value, analytical feasibility, engineering effort, and the potential to serve many customers.

Turn data science into product experiences. Work out how users should interact with model outputs: what they need to see, what needs explanation, how uncertainty should be communicated, and what action they can take next. Help customers understand when a result is ready to act on and when more information or human judgment is needed.

Prototype before committing to a build. Use data analysis, notebooks, lightweight applications, or AI tools to make ideas tangible. Test concepts with customers and work with Data Science to assess whether the underlying approach supports the intended use.

Lead delivery with Data Science and Engineering. Translate discoveries into clear product requirements, scope, and success criteria. Stay involved through development, testing, and launch, resolving tradeoffs and making sure the final experience solves the original problem.

Define quality beyond model performance. Establish how we’ll evaluate analytical validity, usability, reliability, and customer outcomes. Account for incomplete data, uncertain results, and situations where the product should ask for human review.

Learn from real usage. Partner with Customer Impact to support early deployments, observe where users get stuck, and improve adoption. Identify patterns across customers and turn them into reusable product capabilities.

What you’ll bring

• A foundation in data science. You’ve worked on applied problems using statistics, experimentation, econometrics, machine learning, or related methods. You can reason about assumptions, uncertainty, and the limits of an analytical result.

• An interest in owning the product. You want responsibility for choosing problems, defining solutions, making tradeoffs, and measuring what happens after launch.

• Evidence of practical impact. You’ve helped turn an analysis, model, or technical capability into something people used. That might be a customer-facing feature, an internal tool, or a workflow that changed how a team made decisions.

• Hands-on technical ability. You’re comfortable using Python, SQL, or similar tools to explore data, investigate behavior, and test an idea.

• Customer curiosity. You can work directly with users, question an initial request, and uncover the underlying need.

• Clear communication. You can explain technical ideas to nontechnical stakeholders and give technical teams a precise account of the problem and the desired outcome.

• Judgment and follow-through. You can make progress with incomplete information, keep scope focused, and stay accountable through delivery and adoption.

Experience with causal inference, marketing measurement, MMM, attribution, or growth operations is valuable. So is experience building analytical products or working closely with product and engineering teams.

What success looks like

• Customers use the capabilities you own to make and act on growth decisions.

• Analytical results are understandable, appropriately qualified, and connected to useful next steps.

• Product priorities reflect customer needs and a clear understanding of technical feasibility.

• New capabilities move from validated ideas to reliable, adopted products.

• We can demonstrate how the product improves decision-making, saves time, or reduces manual work.

Base salary: $120,000–$200,000 per year, depending on experience.

To apply, send your CV and a short cover letter to careers@bluealpha.ai. Tell us about a time you helped turn an analysis or model into something people used, and what you learned from it.