Job Description
The role involves working on subscription, ads, and in-app LTV prediction services, ensuring the efficiency, reliability, explainability, and observability of ML pipelines. Responsibilities include building and maintaining ETL workflows, querying large tables, detecting anomalies and data drifts, and collaborating with user acquisition managers and data engineers on investigations and data quality issues. The position also entails participation in generative AI projects. Key requirements are hands-on experience with machine learning modeling, proficiency in Python software engineering (including parallel/asynchronous computing, unit testing, CI/CD), a solid understanding of statistics and experimental design, advanced SQL skills for large datasets and ETL, and experience with BI tools for data visualization.
Good To Have:
- Familiarity with MLOps tools and Databricks
- Experience building REST APIs
- Experience with real-time streaming data
- Experience with recommendation systems, NLP/LLMs, or generative AI
- Familiarity with subscription-based product data
Must Have:
- Hands-on experience with ML modeling
- Proficiency in Python software engineering
- Solid grounding in statistics and experimental design
- Advanced SQL skills and ETL experience
- Experience using BI tools for data visualization
Responsibilities
- Work on subscription, ads, and in-app LTV prediction services
- Ensure the efficiency, reliability, explainability, and observability of ML pipelines
- Build and maintain ETL workflows and query large tables
- Detect anomalies, data drifts, and prediction shifts and partner with user acquisition managers on investigations
- Collaborate with data engineers to detect data quality issues
- Participate in generative AI projects
Nice to have
- Familiarity with MLOps tools and the Databricks platform
- Experience building REST APIs and working with real-time streaming data
- Experience with recommendation systems, NLP/LLMs, or other generative AI
- Familiarity with subscription-based product data
Requirements
- Hands-on experience with machine learning modeling and a good understanding of ML fundamentals
- Proficiency in Python software engineering (parallel/asynchronous computing, unit testing, CI/CD, etc.)
- Solid grounding in statistics and experimental design
- Advanced SQL skills and experience working with large datasets, ETL workflows, and scheduling pipelines (e.g., Airflow)
- Experience using BI tools (Tableau, Power BI, and Looker) for data visualization
Victoria Velichko
Talent Acquisition Lead
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