Staff Applied Scientist (Marketing)

Turn scientific insight into business impact.

About Bizee:

Bizee helps entrepreneurs across the US start and grow their businesses, with services spanning company formation and ongoing compliance. We make complex steps easier to navigate so business owners can focus on building what comes next.

About the role:

Help shape how Bizee uses AI to reach and serve more entrepreneurs. As a Staff Applied Scientist, you’ll turn open-ended marketing questions into experiments, models and systems that improve acquisition, conversion and customer value. Your work will connect customer behavior and marketing decisions to measurable business results.

This is a hands-on role where you will define the scientific approach when the problem, available evidence or measures of success are unclear. You’ll investigate data, develop hypotheses, train and adapt models, design experiments, and work with engineers to bring successful solutions into production. You’ll decide which problems deserve investment and establish whether the work produces incremental value.

What you will do:

• Partner with Marketing, Product and Engineering to identify valuable opportunities. Use customer and campaign data to challenge assumptions and translate open-ended problems into testable hypotheses and clear measures of business success.

• Develop models and AI workflows for audience understanding, personalization, recommendations, campaign optimization and content generation. Choose applications based on their potential to improve customer and business outcomes.

• Design controlled experiments to measure incremental improvements in conversion, efficiency and customer value. Use causal methods where randomization is impractical, explain their assumptions and distinguish incremental impact from attributed activity.

• Train, adapt and evaluate models for marketing tasks. Compare statistical baselines, predictive models and LLM-based approaches, choosing methods based on evidence, data availability and operating cost.

• Develop reliable training and evaluation datasets from customer, campaign and product data. Address missing data, selection bias and imbalance, prevent leakage, and respect privacy and consent requirements.

• Create repeatable evaluations that connect model performance to marketing decisions and customer outcomes. For generative applications, test factual accuracy and failure cases using real Bizee examples, expert review and validated automated checks.

• Own the scientific outcome through production and adoption, partnering with engineers to deploy and monitor models, diagnose performance changes, and improve latency and cost. Build feedback loops, track data and model versions, and define when to retrain, revise, or retire a solution.

• Stay close to advances in AI and test the ideas that could improve Bizee’s products. Explain results and trade-offs clearly, document experiments, and share reusable methods that help the wider team make better decisions.

What you need to have:

• A track record in applied science or machine learning of defining problems under uncertainty and taking models from experimentation into sustained business use.

• Strong foundations in statistics, machine learning and experimental design, with depth in causal inference, marketing science, personalization or optimization. You can identify bias, quantify uncertainty and explain what an experiment does and does not establish.

• Hands-on experience developing models for customer behavior or marketing decisions, and applying AI to business workflows. You can choose between predictive models, experimentation and LLM-based approaches rather than treating one method as the default.

• Strong Python and SQL skills, experience with PyTorch or an equivalent framework, and the ability to write tested, maintainable code for reproducible experiments and production collaboration. Use AI tools to accelerate the work while verifying generated code and analysis.

• Experience with customer, product or campaign data and the limitations of observational evidence. You choose meaningful metrics, build representative evaluation sets and connect offline results to measured business impact.

• Sound judgment about AI reliability and sensitive data. You can address unsupported outputs, prompt injection, and data exposure, and design traceable results with appropriate review and fallback behavior.

• The autonomy to identify the problem when goals or evidence conflict, prioritize experiments and make practical trade-offs. You communicate clearly in English and establish methods and decisions that help colleagues work independently.

• A master’s or PhD in computer science, machine learning, statistics, or a related discipline, or equivalent experience demonstrating comparable scientific depth and delivery.

Nice to have:

• Experience with uplift modeling, recommendation systems, customer lifetime value, retention or budget optimization.

• Experience measuring campaign incrementality, working with advertising platforms or combining experimental and observational evidence.

• Experience with LLM-based personalization or content workflows, including evaluation of quality, factual accuracy and downstream customer outcomes.

• Experience operating ML systems in the cloud, or research and open-source contributions that demonstrate original thinking and practical impact.

What we offer

• Competitive compensation 34,000-47,000 PLN/month, stock options, and an annual bonus.

• A flexible benefits allowance to spend on what matters to you.

• An office in the city center and a friendly, non-corporate atmosphere.

• Real ownership and the opportunity to make a visible impact.

Tech stack

• Python
• SQL
• PyTorch
• Machine Learning
• Statistics
• Experimental design