Quick Overview
Job Description
About the Role
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.
Key Accountabilities
1. Product Analytics & Insights
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.
2. Applied Data Science & Modeling
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.
3. Experimentation & Testing
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.
4. Data Infrastructure & Quality
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.
Ideal Background & Qualifications
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, NumPy, 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, BigQuery, dbt) and attribution platforms (Triple Whale, Northbeam, Rockerbox).
Experience applying machine learning models in production.
What Success Looks Like (KPIs)
AI & Integration: AI outputs are reliably interpreted and integrated into product workflows.
Data-Driven Decisions: Product decisions are consistently backed by clear, actionable data insights.
Operationalization: Analytical and models are successfully deployed and actively used in product, growth, or commercial workflows.
Measurable Impact: Work delivers measurable improvements in product performance, growth, commercial outcomes, or decision quality.
Experimentation: Experiments lead to measurable enhancements in product performance.
Data Robustness: Data tracking and pipelines are robust, and quality gaps are identified and addressed early.
Cross-Functional Alignment: Product, Engineering, and Data teams maintain clear alignment on data requirements, model outputs, and operationalisation.
Values
We take our values very seriously and ask that all team members reference (or keep in mind) values in dealing with conflict, challenges, opportunities and day-to-day operations.
Each individual's role is to also hold others accountable and themselves.
Accountability- own your part in it first.
Collaboration- take initiative with discipline and humility.
Resilience- to be steadfast, assess, and bounce back.
Selflessness- ‘what does it take to scale’.
Fun- be present and connect.
The best time to join Valerie was yesterday. The next best time is now 🤓
👋Our Hiring Process
We want interviews to be valuable for both sides. Throughout the process, you'll meet the people you'll work with, learn more about the role, and get a chance to understand how we think and operate.
Our process typically includes:
Introductory conversation with our Talent team <> 30 mins
Hiring Manager interview <> 30-45 mins
Role-specific assessment or practical exercise (where applicable)
Cross-functional or stakeholder interviews <> 30-45 mins
Final conversation with leadership <> 30-45 mins
We'll always let you know what to expect before each stage.
💪🏾What You Can Expect
Meaningful work with visible impact
High ownership from day one
Collaboration with experienced founders, operators, and specialists
A team that values curiosity, initiative, and continuous improvement
Competitive compensation and the tools you need to do your best work
Opportunities to grow as Valerie grows