Founding Data Lead

🔥 1 hour ago

🦀 Maryland – Remote

🦀 Maryland – Remote

💵 $140k / year

⏰ Full Time

🟠 Senior

📊 Data Scientist

👻 Ghost score 0%

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Keep Company

WebsiteLinkedInAll Job Openings

1 - 10 employees

🏥 Healthcare

📣 Marketing

✈️ Travel

💰 $1.4M Seed Round on 2024-04

Healthcare • Marketing • Travel

Keep Company is a business consulting organization that partners with organizations to support parents and caregivers through innovative group coaching programs. They offer a 12-week curriculum led by certified coaches, designed to help members integrate self-care with family and work responsibilities. Their approach emphasizes creating a sense of community and accountability among caregivers, providing tools for fulfillment across all life dimensions. Keep Company aims to enhance the workplace experience for caregivers, helping organizations attract and retain talent through tailored support and community building.

📋 Description

• Own Keep Company’s analytical data foundation, including the warehouse, transformations, pipelines, data quality, and documentation • Establish MotherDuck as a reliable analytical source of truth, including dbt models, source definitions, and durable datasets • Partner with Engineering to improve collection, structure, and maintenance of product, program, survey, HRIS, import, and coach data • Build and maintain pipelines across WorkOS/HRIS integrations, CSV imports, surveys, forms, and platform activity • Structure historical data into clean, member-centric datasets connecting attendance, registrations, survey responses, coaching inputs, relationship activity, and outcomes • Define and maintain a shared taxonomy for constructs, questions, scales, programs, cohorts, and other historical data • Partner with Product and Engineering to define product instrumentation and capture meaningful actions, milestones, decisions, and outcomes • Build reusable measurement layers and reporting products • Develop reporting on relationship participation, engagement, matching quality, and program outcomes • Establish trusted dashboards and recurring analysis for leadership decision-making • Define meaningful metrics and distinguish signals from noise with Product, Programs, Client Success, and leadership • Test and improve assumptions about relationship design, matching, engagement, retention, and development • Own the strategy for moving from descriptive reporting toward predictive and recommendation capabilities • Document, evaluate, and improve the product’s matching logic, including assumptions, objective functions, scoring logic, failure modes, and quality tradeoffs • Build and test models, heuristics, and prototypes to improve matching, identify risk, and support recommendations • Define required data and prioritize collection and instrumentation for future prediction and recommendation use cases • Develop experimentation for model-backed features, including baselines, success criteria, quality monitoring, and iterative improvement • Identify where heuristics are sufficient and where rigorous models are warranted • Build toward use cases such as identifying low-quality matches, detecting disengagement or at-risk programs, and recommending relationship types • Ensure predictive capabilities include privacy protections, tenant isolation, explainability, confidence thresholds, and options for clients with AI restrictions

🎯 Requirements

• 5+ years of strong hands-on experience in data engineering, product analytics, decision science and/or data science at a technology company with a track record of influencing product decisions • Experience with transformation tooling such as dbt, and with building reliable ingestion and transformation pipelines • Deep SQL fluency and experience designing and maintaining warehouse data models • Experience working with messy, incomplete, inconsistent, or historically unstandardized data • Strong background designing well-powered A/B tests, diagnosing bias and variance issues, and interpreting results under real-world conditions • Ability to translate business and product questions into durable datasets, metrics, reporting, and decision-making tools • Experience partnering with Product and Engineering to define instrumentation and turn product behavior into usable data • Working knowledge of statistics, experimentation, clustering, matching, and predictive modeling • Good judgment about data quality, privacy, sensitive attributes, tenant isolation, and consent-based product design • Strong communication skills and comfort explaining data, tradeoffs, 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 building recommendation, matching, prioritization, or prediction systems • Experience evaluating or improving a heuristic-based system before moving to more formal machine-learning approaches • Experience working alongside ML engineers on production models, including offline evaluation, online metrics, and guardrails • Experience building data products or analytics capabilities in a B2B SaaS environment • Experience with employee, engagement, professional-services, people, or relationship data • Experience using modern AI tools or open-weight models responsibly in product or data workflows • Experience helping build or lead a growing data, analytics, or BI function

🏖️ Benefits

• Meaningful equity in our early-stage, venture-backed company • We pay 100% of the premium for two UHC Gold plans and 50% for your dependents • Same approach for dental and vision • 401(k) • FSA • HSA • Commuter benefits • High flexibility culture

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