Measure the business impact of feature rollouts, customer programs, and platform AI features using robust experimentation and causal inference techniques to optimize customer value and cost-to-serve.
Deliver clear, executive-ready recommendations backed by empirical data to product, engineering, and customer success leadership, tracking downstream business outcomes.
Develop and deploy production machine learning models for customer health scoring, cost forecasting, predicted time-to-value, and entity intelligence that link disparate records into unified identities.
Own and govern the semantic layer for product and customer analytics as a versioned product, maintaining certified datasets, metric definitions, and metadata context required by AI agents.
Build and evaluate agentic analytics workflows (such as Genie spaces, Claude skills, and MCP tools) using golden test sets, accuracy monitoring, and regression testing, while partnering with engineering on telemetry and tracking quality.
What You Bring
10+ years of experience in data engineering, analytics engineering, or data science, including 4+ years leading and coaching teams of engineers or scientists.
Hands-on expertise designing, building, and operating production data pipelines and models at scale using SQL and Python, including data orchestration, data quality testing, and event instrumentation.
Strong familiarity with modern data lakehouse architectures; Databricks experience (Spark, Delta Lake, Unity Catalog) strongly preferred, with value placed on Snowflake and dbt.
Proven track record delivering high-impact product insights that influenced executive roadmaps and technical priorities.
Experience owning semantic layers, data governance, and metadata architecture to enable AI agents and self-serve business analytics with lineage and access controls.
Practical expertise applying experimentation, causal inference, and production machine learning (such as predictive scoring, classification, and entity resolution) to product datasets.
Demonstrated experience operationalizing and measuring AI-assisted analytics workflows in production environments.
Bachelor’s degree in computer science, statistics, economics, or a related quantitative field, or equivalent practical experience.
Strong-fit Background Might Include
Data Science Manager or Lead Analytics Engineer at a high-growth SaaS enterprise
Machine Learning / Product Analytics Engineering Lead transitioning to senior people leadership
Enterprise Data Architect or Semantic Layer Lead supporting product engineering organizations
Quantitative Analytics Lead with deep experience in AI tooling, causal inference, and data modeling
Discover our recruiting process and interview tips from our talent acquisition team on our #LifeAtProcore blog.
Additional Information
Base Pay Range
198,240.00 - 272,580.00 USD Annual
This role may also be eligible for Equity Compensation and/or Bonus Incentive Compensation. Procore is committed to offering competitive, fair, and commensurate compensation. Actual compensation will be based on a candidate’s job-related skills, experience, education or training, and location.
For Los Angeles County (unincorporated) Candidates
Procore will consider for employment all qualified applicants, including those with arrest or conviction records, in accordance with the requirements of applicable federal, state, and local laws, including the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act.
A criminal history may have a direct, adverse, and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment: 1. appropriately managing, accessing, and handling confidential information including proprietary and trade secret information, as well as accessing Procore's information technology systems and platforms; 2. interacting with and occasionally having unsupervised contact with internal/external customers, stakeholders, and/or colleagues; and 3. exercising sound judgment.
