• Utilizar el análisis de datos para apoyar a los equipos en la resolución de desafíos del negocio. • Identificar tendencias y patrones en los datos para orientar la toma de decisiones. • Construir y mantener dashboards para el seguimiento de métricas clave del negocio. • Presentar hallazgos basados en datos a stakeholders. • Apoyar proyectos especiales y atender requerimientos analíticos según sea necesario.

About Haus Haus is the incrementality platform leading brands trust to optimize billions in ad spend worldwide. Using frontier causal inference-based econometric models to run experiments, we help brands measure the business impact of marketing, pricing, and promotions with scientific precision. Over $360B is spent annually on paid advertising in the US alone, and the famous quote “half the money I spend on advertising is wasted; the trouble is I don't know which half” still rings true. Haus helps marketers identify which half, and reallocate it to maximize growth. With a founding team of former product managers, economists, and engineers from Google, Netflix, Meta, and Amazon, we make high-quality decision science, incrementality testing, and causal marketing mix modeling accessible to businesses of all sizes—automating the heavy lifting of experiment design, data processing, and insights generation. Haus works with leading brands like FanDuel, Sonos, and Dr. Squatch, delivering ROI gains as high as 30x. Haus is well-capitalized and backed by top-tier VCs, including Insight Partners, Baseline Ventures, Haystack, and others. We're honored that Haus has once again been recognized by LinkedIn as a 2025 Top Startup! What you'll do You’ll be the first dedicated analytics hire at Haus, working directly with the Director of Data & Analytics to build the analytics function. This is a high-leverage role: you’ll touch everything from GTM pipeline reporting to product engagement analysis to the dbt models and warehouse layer underneath. We’re looking for a true generalist who can context-switch fluidly, move fast with modern AI-assisted tooling, and translate data into decisions that executives and cross-functional partners actually act on. Responsibilities GTM Analytics - Build and own pipeline, conversion, and ROI analytics in partnership with Revenue Operations, Marketing, Sales, and Customer Success. - Develop forecasting models for new business acquisition and retention cohorts. - Stand up and own the data warehouse and metric layer (Fivetran for pipelines, dbt for transformations) with GTM as the first domain. - Build scalable, well-tested dbt models that serve as the single source of truth for business metrics. Product Analytics - Analyze feature adoption, engagement patterns, and retention indicators across the product surface. - Build customer health views—both at a portfolio level and drilled into specific product areas. - Partner with Product and Engineering to instrument events and ensure data quality at the source. - Extend the dbt layer and BI tooling (Looker, Mode, Hex, or Tableau) to serve self-serve product analytics across the org. Stakeholder Partnership - Partner with cross-functional leaders (GTM, Product, Finance, Exec team) to embed data into daily decisions. - Translate complex analysis into clear, exec-ready narratives—not just dashboards. - Champion data literacy and build a self-serve analytics culture across the org. Qualifications - 4–7 years in analytics, data, or analytics engineering roles (B2B SaaS strongly preferred). - Hands-on fluency in SQL and dbt; comfort with data modeling and warehouse architecture. - Experience with modern ELT tools (Fivetran or equivalent) and at least one BI platform (Looker, Mode, Hex, Tableau). - Already using AI-assisted coding tools (Claude Code, Codex, etc.) in your day-to-day work. Not just experimenting, but relying on them to ship faster. - Strong stakeholder communication: you can present to a CRO on Monday and pair with an engineer on Tuesday. - Comfort operating as the first analytics hire—you thrive with ambiguity, ownership, and building from zero. Bonus Points - Python proficiency for analysis, scripting, or lightweight ML/statistical modeling. - Experience standing up or significantly evolving a data warehouse (not just inheriting one). - Familiarity with product analytics instrumentation (Rudderstack, Segment,, or similar). - Prior experience in an early-stage or high-growth startup. - Exposure to AI/ML applications in GTM or product analytics (propensity models, lead scoring, churn prediction). What We Offer: We’re a customer-obsessed, high-ownership team focused on driving impact and learning fast. Our environment rewards curiosity, adaptability, and a bias toward action — especially when it helps us better serve our customers. If you're energized by solving dynamic problems, enjoy working in lean, collaborative teams, and thrive in ever-evolving environments, you’ll feel at home here. We invest in exceptional people who want to grow quickly and help shape the future of the company. Some of our benefits include: - Flexible PTO - take time when you need it! - Equity – Startup environment with part-ownership in our successes - Top of the line health, dental, and vision insurance - multiple plan options so you can pick what fits you best - WFH stipend to support the set up you need to be productive - Events & Offsites – opportunities to connect and celebrate in real life! - Free Lunch – Grab a bite on us when you choose to work from the office (hub locations include SF, NYC and Seattle) - New Parent Leave – take time to welcome your newest Hausmate We are remote-friendly however candidates based in commutable distance (50 mi?) to SF, Seattle and NYC will be prioritized as we value frequent in-person collaboration opportunities at Haus. Haus is an equal opportunity employer. We make hiring decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected status. We believe diverse perspectives make us stronger and are committed to an inclusive culture where everyone feels seen, heard, and empowered to contribute. Bring your authentic self — we would love to hear from you.

• Lead quality assurance (QA) of datasets prepared by the Data Officer and Data Associate to ensure accuracy, consistency, and completeness. • Standardize data structures, collection formats, and documentation across projects. • Maintain data processing SOPs and coordinate harmonization of project databases (farmer data, farm plots, training records, monitoring indicators). • Analyze agricultural and project datasets to generate insights on smallholder participation, farm characteristics, production, traceability, and program performance. • Produce dashboards, visualizations, and analytical summaries to support project teams. • Prepare and review data inputs for project and donor reporting (e.g., monthly and quarterly reports, monitoring indicators). • Review and develop AI training datasets and knowledge content on agronomic practices for the AI chatbot, ensuring accuracy, relevance to local farming contexts, and usability for project implementation and farmer support. • Together with the Data Associate, serve as an administrator responsible for managing, and maintaining project dashboards and project data systems. • Ensure project indicators and datasets are updated, integrated, and accessible. • Support development and tracking of project monitoring and evaluation (M&E) indicators. • Conduct analysis to assess program progress, outcomes, and key trends. • Provide technical guidance to Data Officers on data management and analysis. • Coordinate with project managers and field teams to ensure smooth data collection, processing, and reporting workflows. • Collaborate with partners and data vendors to ensure data quality standards are implemented.

• Gathering relevant company contact information, such as email addresses, job titles, and external and URLs (i.e. LinkedIn), for cold outreach and marketing campaigns • Ensuring 100% data quality and accuracy, meticulously verifying the accuracy of the data to ensure that the data can be trusted for further use • Identifying potential leads and prospects; sourcing contact information for those parties • Assistance in the creation of marketing campaign performance reports and supporting analyses • Interpreting data, analyzing results using statistical tools, and providing ongoing reports on marketing campaign performance • Providing quality analysis and assurance for imported data from various external sources • Alignment of contact sourcing strategies with overall business goals and marketing activities • Clear arrangement of the data in accordance with the demands of various projects • Organizing CRM and contact information data into lists that are tailored to meet the data quality requirements • Periodic reviews and CRM contact database updates to ensure data accuracy and usefulness