Data Scientist

We are hiring a Data Analyst / Data Scientist to drive growth, commercial and product analytics, applied data science, and AI-driven capabilities across Valerie's Brands and Investment Growth Platform.

In this role, you will work across product analytics, modeling, experimentation, and data workflows, using SQL and Python to turn complex business and product questions into actionable insights and production-ready analytical solutions. You will collaborate closely with Product, Engineering, and Data teams to operationalize models, shape how AI and model outputs are integrated into the product, validate model-driven decisions, and build data systems that continuously enhance product and commercial performance.

• Define and track key product metrics, including user behavior, funnels, drop-offs, and feature performance.

• Translate complex product questions into structured analysis and actionable recommendations that directly influence product, growth, commercial, or investment decisions.

• Build and apply models for segmentation, scoring systems, classification, and recommendations, ensuring alignment with product use cases and practical deployment.

• Partner with Engineering and Data teams to operationalize models and analytical workflows to ensure production-grade reliability.

• Work effectively with incomplete, imperfect, or banded data—making assumptions explicit and identifying gaps that materially affect analysis or decision-making.

• Own analytical and modeling projects end-to-end: from defining the business problem and data requirements through modeling, validation, deployment, and impact measurement.

• Build and apply forecasting, unit economics, scoring, incrementality, media effectiveness, or other decision models relevant to commercial and product use cases.

• Design and run experiments, including A/B testing and cohort analysis.

• Measure the impact of product changes, AI outputs, and workflow adjustments.

• Build feedback loops to continuously improve product and model performance over time.

• Quantify uncertainty, test assumptions, and clearly communicate the limitations and confidence levels of analytical conclusions.

• Define data requirements for new product features and ensure data tracking is accurate, consistent, and complete.

• Collaborate with engineering to structure datasets and pipelines for analysis and modeling.

• Identify and resolve gaps in data visibility.

• Experience: 6–8 years of relevant analytics or data science experience, with demonstrated ownership of meaningful analytical or modeling work.

• Problem-Solving: Strong ability to translate ambiguous business, product, growth, or commercial problems into structured data questions and actionable analytical approaches.

• Domain Knowledge: Experience working with product metrics, funnels, and user behavior analysis. Experience in growth, DTC/e-commerce, marketplace, subscription, ad-tech, agency, or unit-economics-driven environments is preferred.

• Modeling Expertise: Experience building and applying classification, regression, clustering, scoring, forecasting, and decision systems, with evidence of taking at least one meaningful model or analytical product into real business use.

• Commercial Acumen: Strong understanding of growth and commercial metrics (CAC, AOV, COGS, contribution margin, payback period, breakeven) to inform business decisions.

• Technical Stack:

  • Strong SQL skills.

  • Experience with structured and semi-structured data.

  • Strong working proficiency in Python for data analysis, modeling, experimentation, and automation (R experience is acceptable if backed by equivalent analytics capability).

  • Proficiency with libraries such as pandas
    , Num Py
    , and scikit-learn
    .

• Experimentation & AI: Experience with A/B testing, statistical analysis, measurement, causality, and impact evaluation. Experience evaluating LLM/AI-generated outputs in product workflows (understanding response structures, quality, consistency, and reliability).

• Nice-to-Haves:

  • Exposure to data visualization tools (Tableau, Power BI, Metabase).

  • Experience in zero-to-one startup environments.

  • Experience with media mix modeling, incrementality, causal inference, experimental design, or media effectiveness measurement.

  • Experience with modern cloud data warehouses/workflows (Snowflake, Big Query, dbt) and attribution platforms (Triple Whale,…