1848 Ventures, backed by Westfield

Growth Marketing

• Join as the first dedicated marketing hire, helping build a B2B Saas company serving SMB in-home sales teams.
• Drive go-to-market strategy, validating demand, and shaping the SPCloser brand.
• Own growth end-to-end – from positioning and messaging to reporting on acquisition, activation, and revenue.
• Collaborate cross-functionally to test early hypotheses, learn quickly, and build repeatable growth engines from the ground up.
• Design and run experiments that drive customer acquisition and activation across PLG and founder-led sales channels.
• Own the self-serve sign-up and onboarding funnel, running experiments to improve activation, time-to-value, and trial-to-paid conversion.
• Interview customers and prospects to understand workflows, objections, and language – and turn insights into messaging and campaigns.
• Run a steady cadence of small experiments across website, email, and in-product touchpoints, doubling down on what works.
• Set up and maintain a lightweight but reliable growth stack (Webflow, HubSpot, analytics) to track the funnel and guide decisions.
• Test and scale a focused mix of paid channels (Google, Meta, LinkedIn) using freelancers or agencies where it adds leverage.
• Define and refine ICPs and buying journeys across self-serve and sales-assisted paths to keep marketing, product, and sales aligned.
• Use product usage and CRM data to identify high-intent signals and trigger lifecycle campaigns.

Founding Growth Marketer – SPCloser

Full-stack Engineer

• Lead platform and data teams responsible for core services, data layers, and end-to-end data flow
• Own the platform architecture for core services and data: ingestion → transformation → storage → serving → observability
• Define data contracts and schemas between ingestion, transformations (e.g., dbt), ML features, and downstream applications
• Establish standards for orchestration, CI/CD, code quality, documentation, and reproducibility across platform and data repos
• Set and enforce SLOs for reliability, latency, and data freshness; implement guardrails and automated quality checks
• Drive cost-aware design (compute/storage) and performance tuning for batch and streaming workloads
• Translate the roadmap into scoped work with crisp acceptance criteria, success metrics, and rollout plans
• Coordinate cross-team dependencies (Product, ML, Integrations) to keep the value stream flowing end-to-end
• Build and operate a pragmatic observability stack (logs, metrics, tracing, data quality monitors) with alerting
• Shape the team (platform engineers, data engineers, integrations engineers); partner with recruiting; run a structured loop
• Document engineering standards: coding conventions, review SLAs, on-call expectations, incident hygiene
• Run weekly 1:1s with agendas; give timely, specific feedback; co-create development plans and manage underperformance
• Keep architecture diagrams, runbooks, READMEs, and data lineage current; promote a docs-as-code culture
• Provide weekly progress, risks, and asks to the CTO; share monthly demos/notes with broader stakeholders

Technical Lead and Manager, Platform & Data

• Own the platform architecture for core services and data: ingestion → transformation → storage → serving → observability
• Define data contracts and schemas between ingestion, transformations (e.g., dbt), ML features, and downstream applications
• Establish standards for orchestration, CI/CD, code quality, documentation, and reproducibility across platform and data repos
• Set and enforce SLOs for reliability, latency, and data freshness; implement guardrails and automated quality checks
• Drive cost-aware design and performance tuning for batch and streaming workloads
• Translate the roadmap into scoped work with crisp acceptance criteria, success metrics, and rollout plans
• Prioritize, sequence, and unblock; ship iteratively with progressive delivery and clear rollback paths
• Coordinate cross-team dependencies (Product, ML, Integrations) to keep the value stream flowing end-to-end
• Build and operate a pragmatic observability stack (logs, metrics, tracing, data quality monitors) with trusted alerting
• Partner with Integrations on PMS/booking-engine data flows; design resilient interfaces and backfills
• Shape the team (platform engineers, data engineers, integrations engineers); partner with recruiting; run a structured loop
• Document engineering standards, runbooks, READMEs, and data lineage; promote a docs-as-code culture
• Provide weekly progress, risks, and asks to the CTO; share monthly demos/notes with broader stakeholders
• Player–coach role reporting to the CTO and collaborating closely with Product, ML, and Customer Success

Technical Leader and Manager, Platform & Data

Machine Learning Engineer

• Design, train, and deploy production ML models, with emphasis on tree-based methods (XGBoost, LightGBM, CatBoost).
• Contribute to feature engineering and model evaluation approaches that balance accuracy, interpretability, and speed.
• Translate customer needs and product requirements into practical modeling tasks (classification, regression, ranking, uplift).
• Develop custom performance metrics to evaluate models’ ability to generate reasonable counterfactual demand estimates while accounting for varying customer product expectations.
• Support explainability efforts to justify rate decisions with clear artifacts and documentation.
• Build and maintain training and evaluation pipelines, ensuring offline metrics align with online business outcomes.
• Partner with engineering to integrate models into production services and batch jobs with defined SLAs.
• Contribute production ready Python and SQL code with strong testing and review practices.
• Support data quality, observability, and reproducibility across the ML lifecycle.
• Collaborate on experiment design, including A/B testing and staged rollouts.
• Implement policy guardrails to ensure automated rate changes are safe and controlled.
• Analyze experimental results, communicate lift and trade-offs, and document findings.
• Work closely with product, engineering, and leadership to scope problems and propose modeling approaches.
• Participate in code reviews, model reviews, and planning sessions.
• Share learnings and contribute to improving team practices around modeling, metrics, and documentation.

Founding Growth Marketer – SPCloser

Technical Lead and Manager, Platform & Data

Technical Leader and Manager, Platform & Data

Senior Machine Learning Engineer – Applied Modeling