About the Role
UMATR has partnered with a well-funded, venture-backed startup focused on building a smarter, more inclusive real estate investment platform. This platform aims to provide personalized, data-driven investment opportunities that are scalable, intuitive, and engaging. Machine learning is a core component of their product, driving aspects from discovery and marketing to critical decision-making processes. You will be instrumental in establishing and leading the ML & AI function, requiring experience in building products from scratch within a fast-paced environment.
What You’ll Do
- Architect and build machine learning models for:
- Personalized investment recommendations
- Search relevance and ranking
- Lead scoring, conversion prediction, and dynamic pricing
- Audience segmentation and targeting for growth/marketing
- Own the full ML lifecycle: from data collection and preprocessing to model development, evaluation, deployment, and monitoring.
- Introduce technologies and shape the roadmap for the ML function.
- Collaborate closely with engineering, product, and marketing teams to translate business needs into ML solutions.
- Set up scalable, maintainable ML pipelines and infrastructure from the ground up.
- Explore and apply GenAI (e.g., embeddings, summarization) to enhance discovery and engagement for future growth.
What We’re Looking For
- 7+ years of experience in applied ML or data science, specifically in tech or customer-facing products.
- Strong understanding of classical ML techniques for regression, classification, clustering, and ranking.
- Proven experience in building recommender systems, search infrastructure, or predictive models in production.
- Extensive background and expertise within Python.
- Previous roles where you have technically led and taken ownership in decision making, architecture and development.
- Solid coding and software engineering practices (version control, CI/CD, testing).
- Strong deployment and monitoring experience.
- End-to-end experience - building from the ground up.
- Experience working with business and product stakeholders to deliver measurable results with ML.
- Ability to balance technical depth with product impact.