Founding Data Lead

This is a fully remote position, open to applicants in Maryland.

📋 Description

• Take ownership of Keep Company's analytical data infrastructure, which encompasses the data warehouse, transformations, pipelines, data quality, and documentation.

• Position MotherDuck as a trustworthy analytical source of truth by developing dbt models, source definitions, and robust datasets.

• Collaborate with Engineering to enhance the collection, structuring, and maintenance of data related to products, programs, surveys, HRIS, imports, and coaching.

• Create and manage data pipelines across WorkOS/HRIS integrations, CSV imports, surveys, forms, and platform activities.

• Organize historical data into clear, member-focused datasets that link attendance, registrations, survey responses, coaching inputs, relationship activities, and outcomes.

• Establish and uphold a common taxonomy for constructs, questions, scales, programs, cohorts, and other historical data.

• Work alongside Product and Engineering to define product instrumentation and capture significant actions, milestones, decisions, and outcomes.

• Develop reusable measurement frameworks and reporting tools.

• Produce reports on relationship participation, engagement, matching quality, and program results.

• Create reliable dashboards and recurring analyses to assist leadership in decision-making.

• Identify essential metrics and differentiate between valuable signals and noise in collaboration with Product, Programs, Client Success, and leadership.

• Test and refine hypotheses regarding relationship design, matching, engagement, retention, and development.

• Lead the strategy for transitioning from descriptive reporting to predictive and recommendation functionalities.

• Document, assess, and enhance the product's matching logic, including its assumptions, objective functions, scoring methods, failure scenarios, and quality trade-offs.

• Construct and evaluate models, heuristics, and prototypes to enhance matching, identify risks, and support recommendations.

• Specify necessary data and prioritize its collection and instrumentation for future predictive and recommendation applications.

• Design experiments for model-driven features, incorporating baselines, success criteria, quality monitoring, and iterative enhancements.

• Determine where heuristics suffice and where rigorous modeling is essential.

• Aim to develop use cases that identify low-quality matches, detect disengagement or at-risk programs, and provide recommendations for relationship types.

• Ensure that predictive capabilities uphold privacy protections, tenant isolation, explainability, confidence thresholds, and options for clients with AI constraints.

⛳️ Requirements

• A minimum of 5 years of substantial hands-on experience in data engineering, product analytics, decision science, and/or data science within a technology company, demonstrating the ability to influence product decisions.

• Proficient in transformation tools such as dbt, with experience in creating reliable ingestion and transformation pipelines.

• Advanced SQL skills and experience in designing and maintaining warehouse data models.

• Familiarity with handling messy, incomplete, inconsistent, or historically unstandardized data.

• Strong expertise in designing robust A/B tests, diagnosing bias and variance issues, and interpreting results in real-world scenarios.

• Capability to translate business and product inquiries into enduring datasets, metrics, reporting, and decision-making instruments.

• Experience collaborating with Product and Engineering teams to define instrumentation and transform product behavior into actionable data.

• Working knowledge of statistics, experimentation, clustering, matching, and predictive modeling.

• Sound judgment concerning data quality, privacy, sensitive attributes, tenant isolation, and consent-driven product design.

• Excellent communication skills with the ability to convey data, trade-offs, uncertainty, and model behavior to non-technical stakeholders.

• Preferred experience with MotherDuck, DuckDB, Aurora/Postgres-derived data, S3, Parquet, WorkOS, Hex, or similar tools.

• Experience in developing recommendation, matching, prioritization, or prediction systems.

• Background in evaluating or enhancing a heuristic-based system before advancing to more formal machine-learning methodologies.

• Hands-on experience working alongside ML engineers on production models, including offline evaluation, online metrics, and guardrails.

• Proven experience in building data products or analytics capabilities within a B2B SaaS context.

• Familiarity with employee data, engagement, professional services, people data, or relationship analytics.

• Responsible use of modern AI tools or open-weight models in product or data workflows.

• Experience in helping shape or lead a growing data, analytics, or BI function.

🏝️ Benefits

• Meaningful equity in our early-stage, venture-backed company.

• We cover 100% of the premium for two UHC Gold plans and 50% for your dependents.

• The same approach applies for dental and vision coverage.

• 401(k) plan.

• Flexible Spending Account (FSA).

• Health Savings Account (HSA).

• Commuter benefits.

• A high-flexibility culture.