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
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