Software Engineer II
Software Engineer II (data science/ML) at ZoomInfo in Toronto: own R&D projects end-to-end, from scoping business problems and KPIs to building predictive models and deploying production pipelines on large datasets using Python, SQL, Snowflake, Databricks, and GCP to support Product, Sales, Marketing, Finance, and HR analytics.
- Compensation
- Not disclosed
- Location
- Toronto, Canada
- Work setup
- Not specified
- Seniority
- Middle
- Employment
- Not specified
- Experience
- 2+ years
- Category
- Software engineering
- Posted
Skills
Must have
- Master's degree in a STEM or quantitative field (computer science, mathematics, engineering, or related)
- 2+ years professional experience building and deploying data science or ML solutions, including at least one production deployment
- Experience translating business questions into research problems and communicating findings to technical and non-technical audiences
- Proficiency in Python and SQL, and experience with data visualization tools such as Tableau
- Experience with cloud data and ML platforms such as Snowflake, Databricks, or Google Cloud Platform
Nice to have
- Doctoral degree in a STEM or quantitative field
- Familiarity with MLOps practices (model versioning, monitoring, drift detection, CI/CD for ML)
- Experience presenting analyses to senior technical and non-technical audiences
- Exposure to Sales, Marketing, Finance, or HR analytics domains
Full description
ZoomInfo is where careers accelerate. We move fast, think boldly, and empower you to do the best work of your life. You’ll be surrounded by teammates who care deeply, challenge each other, and celebrate wins. With tools that amplify your impact and a culture that backs your ambition, you won’t just contribute. You’ll make things happen–fast.
As a Software Engineer II, you will leverage theory, data, and research to solve business problems. You will support R&D and analytics efforts across multiple areas of the business including Product, Sales, Marketing, Finance, HR, and related functions.
This role will be responsible for drawing insights from large data sets, defining and implementing key model performance indicators, and for communicating insights and trends to support business decision-making. Effectiveness in this position will require an understanding of technical methods and data engineering necessary to build and implement AI solutions, as well as knowing general industry trends, business objectives, and workforce dynamics. You will use data to develop insights, forecasts, metrics, dashboards and recommendations to inform decisions about our operations and go-to-market strategy.
What You’ll Do (Essential Functions)
Own research and development projects end-to-end. Scope ambiguous problems and define success metrics, through model development, deployment, and ongoing monitoring.
Translate business questions from various business units into well-defined problems with clear KPIs, deliverables, and timelines.
Assess the feasibility, expected impact, and tradeoffs of proposed initiatives to inform prioritization and scoping decisions with cross-functional partners.
Design and conduct rigorous statistical analyses and build robust predictive models to answer complex business questions, selecting appropriate methods (e.g., regression, time series, survival/attrition, hypothesis testing, graph and network analysis) for the problem at hand.
Design and implement production-ready pipelines that can handle large-scale datasets, leveraging distributed processing systems (e.g., Snowflake, Databricks, Google Cloud Platform) and building reusable, modular components.
Write readable, well-structured, modular code using standard libraries, and ensure your work is tested, documented, and maintainable by others.
Participate in code and design reviews, giving and receiving constructive feedback to raise the quality bar across the team.
Contribute to shared practices, tooling, and documentation that improve how the data science function operates.
Share knowledge with teammates and provide informal guidance to more junior colleagues on modeling, code, and analysis approaches.