Data Intelligence & Matching Operations Lead

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Description:

Remote / Hybrid (preferred near hotel or partner markets) About Shwcase: Shwcase is a platform that intelligently connects small brands with physical spaces — starting with hotels — to help them grow visibility, sales, and real-world presence. By leveraging data on audiences, performance, and location dynamics, Shwcase makes it easy for brands to test and expand into new markets while helping property owners activate underutilized space. Our mission is to democratize access to opportunity and enable smarter commerce in the real world. Overview: We're seeking a Data Intelligence & Matching Operations Lead to structure, implement, and manage the data ecosystem behind our brand-space matching engine. This role begins with a 3-month POC (proof of concept) phase, focused on one hotel property where guest demographics vary seasonally. If successful, the role will extend into a broader pilot, followed by beta testing and full launch. This person will build the foundational system that powers Shwcase’s intelligence engine — manually at first, with an eye toward scalable automation. This individual should have a strong grasp of structured data systems and a creative mindset to help define and refine the algorithm, including surfacing novel data points that could improve brand-space fit over time. Key Responsibilities: 1. Design & Structure the Data Flow Lead the creation and refinement of brand, space, place (hotel), and individual sign-up forms Ensure form fields support downstream use in scoring, segmentation, and feedback Standardize inputs across dropdowns, enums, and tags (e.g., price tier, product use case, demographics) 2. Build & Manage the Integrated Data Model Design and maintain clean, normalized datasets that link: Brand profiles Space opportunities (with space-level detail) Places (e.g., hotel-level data like guest demos, city, dwell time) Feedback and performance data (post-activation, sales, etc.) Integrate internal and external data sources: Internal: onboarding forms, engagement tracking, team feedback, internal calendars External: POS systems, foot traffic APIs, psychographic overlays, public events 3. Operationalize the Matching Algorithm Translate the brand-space scoring logic into a working model Run the match engine and generate top-N brand-space recommendations Normalize inputs like audience alignment, price compatibility, deal structure, engagement potential Propose and test new data variables that may enhance recommendation quality 4. Performance Monitoring & Optimization Track performance of placements through feedback loops and sales signals Iterate on scoring model based on engagement and outcome trends Collaborate with team to improve field design and data hygiene 5. Build for Scale and Handoff During the POC and Pilot phases, maintain the system manually or through low-code tools (e.g., Airtable, Google Sheets) As we progress toward Beta and Launch phases, help define automation needs and transition portions of the process to engineering or operational owners Contribute documentation and onboarding material for long-term system maintenance Bonus Responsibilities (Optional): Own the internal dashboard for brand-space performance and scoring Help implement low-code tools (e.g., Airtable, Retool) for data ops Work with product to prepare data for eventual ML model training Ideal Experience & Tools: 2+ years working in data ops, product ops, analytics, or marketplace environments Proficiency with Google Sheets, Forms, and databases (SQL, Airtable) Comfortable with scoring systems, structured data, and form logic Experience with CRM, POS, or location-based data is a strong plus Interest in emerging brands, pop-up retail, and place-based commerce Key Skills: Data modeling and structuring for business decision-making Form design (Google Forms, Typeform, Airtable) with downstream logic in mind Experience with structured data (tags, enums, nested relationships) Operational fluency with tools like Google Sheets, Airtable, or Notion Ability to implement and evolve scoring logic or rules-based recommendation engines Cross-functional communication (between product, operations, and data) Comfort with feedback loops, experimentation, and iteration Optional: working knowledge of SQL, Python, or low-code tools (e.g., Zapier, Retool) Commitment & Timeline: Phase 1 (POC): 3 months focused on one hotel Estimated weekly commitment: 12–20 hours/week If successful, the role will extend to a broader pilot, followed by Beta testing and full launch Candidate may remain in role as system scales or help transition setup to internal teams or engineers Success Looks Like: Scoring model operational and generating weekly recommendations Brand, space, and hotel databases are clean and interconnected Team has access to accurate, actionable data on match quality and performance Forms and systems evolve efficiently as the product scales To Apply: Please apply with: A short note on why you are the best fit for this role An estimate of the time you can commit to Phase 1 (POC) A rough estimate of how long you believe Phase 1 should take and what key deliverables you would aim to complete A brief description of how you would approach structuring Phase 1 One or two novel data points you would consider introducing to improve brand-space matching (Optional) A link to a dashboard, scoring model, or system you've built that’s relevant to this role

Tags: SQL, Python, Data Analysis, Platform Designer, Google Sheets, Notion

Keyword: Machine Learning

Job Type: Hourly