Zipify builds high-impact Shopify apps that help merchants maximize revenue and conversion. Our flagship product, OneClickUpsell (OCU), has generated over $1.2B in upsell revenue for merchants. We are evolving beyond traditional SaaS into a hybrid model that combines product, AI, and high-value services to deliver measurable growth for ecommerce brands.
We’re looking for a Data Scientist / ML Engineer who is equally comfortable working with data and building AI-powered systems. This is not a role for someone who only trains models — we need someone who understands data end-to-end: from exploring and cleaning it, to building intelligent features on top of it. If you have a background in BI, data analytics, or data engineering and have grown into ML and AI — this might be a great fit. What We Value in a New Master * 3+ years of hands-on Python experience (pandas, NumPy) * Strong SQL skills — writing complex queries, working with nested and JSON data structures, query optimization, and understanding of normalization principles * Experience with EDA, data quality, and working with messy real-world data * 1+ years of ML experience: classification, clustering, regression * Experience working with LLM APIs and prompt engineering * Understanding of agentic system design * Git, Docker — comfortable day-to-day usage * English: Upper-Intermediate or higher
Nice to Have * Background in BI analytics or data engineering (Airflow, dbt, BigQuery) * Experience with Great Expectations or similar data quality frameworks * Familiarity with dbt * Power BI or other BI tools experience * Experience with recommender systems or A/B testing * AWS stack: SageMaker, S3, Lambda * Experience with Streamlit * Background in e-commerce, SaaS, or product-driven environments * Portfolio with relevant projects
How You’ll Contribute This role sits at the intersection of ML and data, which means you may contribute to the BI team when ML workload allows. We see this as an opportunity, not a fallback — the ideal candidate is comfortable switching contexts and adding value across data disciplines.
You might be asked to: * Set up or extend data quality checks using Great Expectations * Edit or extend existing dbt models * Build or update reports in Power BI * Contribute to data pipeline improvements alongside the data engineering team
You’ll work on: * Exploratory data analysis and pattern detection in customer behaviour * Data quality monitoring and improvement * Product analytics to support decision-making * Building recommendation and decisioning systems * Designing and integrating RAG pipelines and LLM-powered features * Working with LLM APIs (OpenAI, Claude, etc.) and agentic systems
