Head of Analytics

About Leptex

Leptex is building personal intelligence for everyday people. Our mission is to democratize AI: make it something everyone can use, understand, and control. Each person should decide how their data is used, what their AI can do, and which decisions remain theirs. We start with farmers and independent business owners, building alongside them and learning from their daily work. Join us to bring that power to more people.

About the role

As Head of Analytics, you will lead Leptex's analytics work and help shape the company. You will study user behavior, product performance, customer growth, and physical operations, combining quantitative analysis with interviews and observations to support useful decisions for Leptex and the businesses we serve. You will work with the founder and collaborate across product, engineering, legal, and robotics as these functions develop.

Your work will address questions such as how many paying customers Leptex could serve next year, why people adopt or stop using a product, which changes improve their experience, and what a business should prepare for the next season. You will develop forecasts, improve each customer's dashboard, and build analysis into products that people can use. You will explain the evidence, assumptions, and uncertainty behind the results.

Our Master Plan connects personal intelligence with a longer-term exploration of production, robotics, and fulfillment. Start with software use, customer dashboards, and available business records. As equipment integrations and field trials are validated, extend the analysis to physical operations, including task quality, capacity, human intervention, and cost. Use AI tools to develop and improve these capabilities quickly while checking the data, code, and conclusions yourself.

We value exceptional learning ability, sound statistical reasoning, and evidence that you can turn analysis into a useful product. We do not require a particular degree, a minimum number of years of experience, or a previous leadership title. This is a full-time remote role, and you must live and work in the United States.

Compensation: Cash compensation and any equity arrangements have not yet been determined. Compensation, any applicable benefits, and any equity grant and vesting terms will be agreed in writing by both parties before work begins.

What you'll do

• Set the analytics strategy and metrics: Work with the founder to choose the questions that matter most and establish consistent definitions for customers, active use, inquiries, confirmed orders, and completed work. Distinguish Leptex's business customers from the buyers they serve, and connect metrics to decisions and accountable owners.
• Forecast growth and demand: Model customer acquisition, retention, and churn for Leptex, and forecast customer numbers and order demand for the businesses we serve. Develop forecasts for annual planning and shorter-term decisions using appropriate methods, explicit assumptions, and uncertainty ranges. Compare forecasts with simple baselines, evaluate them against data from later periods, and update them as results arrive.
• Build useful customer dashboards: Turn reliable data into clear views of activity, orders, repeat business, service costs, and relevant forecasts for each customer. Explain what changed, what may need attention, and where the evidence is limited. Make definitions, data freshness, and the basis of important conclusions understandable.
• Establish reliable data foundations: Design practical data models, SQL queries, and pipelines connecting authorized product events, customer records, transactions, and operational data. Check missing values, duplicates, joins, timestamps, and consistency. Keep analyses reproducible and investigate discrepancies before using results in decisions.
• Use AI to iterate on data products: Use AI assistants and coding tools to explore data, write and review queries and code, create visualizations, and build working analytical features. Test and deploy small improvements with product collaborators, check generated work, and learn from actual customer use.
• Understand user behavior and evaluate products: Combine interviews and observations with funnel analysis, cohort analysis, and appropriate experiments to understand how people discover, adopt, and use products, and why they stop using them. Study where they hesitate, what builds trust, and which changes improve their experience. Distinguish correlation from causal evidence, account for limited samples and selection effects, and explain what the evidence can support.
• Improve supply and fulfillment decisions: Combine authorized order records, unmet demand, inventory, production conditions, and fulfillment capacity to compare operating scenarios. Account for stockouts, seasonality, and incomplete records. Test whether recommendations improve planning while keeping the business owner in control of commitments.
• Connect software and hardware evidence: Work with robotics collaborators and operators to define measurements for controlled trials and analyze sensor readings, equipment logs, and task outcomes. Measure completion quality, damage, downtime, human intervention, and cost per task, and use the findings to improve products and operating decisions.
• Protect customer data and control: Build analysis around clear purposes, appropriate access, and customer permissions. Preserve separation between businesses and work with legal and engineering on retention, deletion, and any explicitly authorized analysis across businesses. Check that dashboards and AI tools expose only the information each user may access.
• Build the analytics function and the company: Share responsibility for company priorities, resource choices, and long-term decisions. Help recruit and develop analysts and other specialists as needs become clear, establish useful review and monitoring practices, and remain directly involved from analysis and modeling through product delivery.

What we're looking for

• Exceptional learning ability: You quickly develop a deep understanding of unfamiliar research methods, statistical techniques, AI tools, business models, and physical processes. You find reliable sources, test your understanding, and show how new knowledge improved an analysis, decision, or product.
• Statistics and forecasting: You understand probability, uncertainty, time series, and model evaluation. You can choose suitable baselines, validate on data unavailable during training, avoid using future information, and recognize when sparse or changing data limits a forecast.
• SQL and practical programming: You can query and model data, write and debug analytical code in a language such as Python, and build maintainable pipelines and tests. You can trace a result to its source data and resolve problems involving missing records, inconsistent definitions, or incorrect joins.
• Experience building data products: You have built or materially improved a dashboard, forecasting tool, analytical feature, or decision-support product that people have used. You can explain your contribution, validation, and lessons from feedback. Research, personal projects, open-source work, and professional experience can all demonstrate this ability.
• AI tool fluency: You use AI assistants, coding agents, and automation to speed up research, analysis, visualization, and product development. You inspect generated queries, code, and explanations, verify important results, and protect confidential information when choosing tools and data.
• Experimental and analytical judgment: You can turn a vague question into a measurable outcome and a suitable study. You distinguish observation from explanation, identify confounding and selection effects, and communicate what an experiment or analysis can and cannot establish.
• Software and hardware systems thinking: You can connect product behavior with physical operating conditions and learn from sensor data, equipment logs, and field observations. You reason about timing, measurement error, reliability, capacity, and human intervention, and work effectively with specialists.
• User understanding and clear communication: You can investigate people's needs, motivations, and working conditions through interviews, observation, and product evidence. You explain models, charts, and uncertainty in plain language and communicate effectively in English. You adapt dashboards and metrics to different workflows and markets, focusing on decisions that help people run their businesses.
• Commitment to personal AI sovereignty: You want people to control their AI, data, and important decisions. You can turn that commitment into appropriate access, understandable explanations, and meaningful choices about how information is used, including when analysis spans products or businesses.
• Leadership and ownership: You are ready to make a sustained commitment to building the company, share difficult decisions, and take responsibility for results. You can set priorities with limited resources, help others develop, revise your judgment when evidence changes, and carry work through to a useful product.

Equal opportunity

Leptex is an equal opportunity employer. We base hiring decisions on job-related skills and qualifications. We do not discriminate against applicants or employees based on race, color, religion, sex, pregnancy, sexual orientation, gender identity, national origin, age, disability, genetic information, veteran status, or any other characteristic protected by applicable law.

If you need a reasonable accommodation during the application or interview process, write to careers@leptex.com.

We do not use AI to screen or assess applicants. A person reads every application. We use application materials to assess your suitability for a role, communicate with you about recruitment, and meet applicable legal and recordkeeping obligations. We retain them only for as long as needed for these purposes, including any required retention period or legal hold, and then delete them. For questions or requests about your application data, contact privacy@leptex.com.