Data Science Engineer
$163,200–$236,400 year
On-siteSan Francisco, California, United States or Seattle, Washington, United States
Job Summary
Drive data science modeling initiatives for forecasting and analysis, focusing on customer retention metrics, units, and ARR. Automate, enhance, and maintain analytic applications and data structures used for modeling while monitoring weekly performance to identify root causes of metric changes. Partner with finance and business stakeholders to develop forecasting processes leveraging automation and AI/ML techniques, presenting findings to senior leadership. Work with the PowerBI team to deliver dashboards tracking key indicators and collaborate with data engineering teams to reverse engineer data flows and implement source-to-target mappings. Prepare analytical reports documenting financial models and explore large datasets to identify trends and operational problems.
Required Qualifications
- Strong proficiency in SQL
- Strong proficiency in Python
- Experience in data cloud platform (eg. Databricks, AWS, Snowflake)
- Understanding of machine learning techniques, specifically involving supervised learning applied to time-series analysis
- Proficiency with data visualization tools (e.g. Power BI, Tableau)
- Ability to dig-in, understand the data, and to use creative thinking and problem-solving skills
- History of deriving and communicating actionable insights from analysis projects to product and/or business leaders
- Degree in a quantitative field like statistics, economics, applied math, operations research or engineering
- or related work experience
Desired Qualifications
- R is a plus
- Knowledge of Microsoft Office, specifically Power Query, M/DAX
- Understanding of Version Control frameworks such as GitHub
- Knowledge of customer cancellation forecasting and cohort retention
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