About the role
Laurel is on a mission to return time. As the leading AI Time platform for professional services firms, we’re transforming how organizations capture, analyze, and optimize their most valuable resource: time. Our proprietary machine learning technology automates work time capture and connects time data to business outcomes, enabling firms to increase profitability, improve client delivery, and make data-driven strategic decisions. We serve many of the world's largest accounting and law firms, including EY, Aprio, Crowell & Moring, and Frost Brown Todd, and process over 1 billion work activities annually that have never been collected and aggregated before Laurel’s AI Time platform. Our team comprises top talent in AI, product development, and engineering—innovative, humble, and forward-thinking professionals committed to redefining productivity in the knowledge economy. We're building solutions that empower workers to deliver twice the value in half the time, giving people more time to be creative and impactful. If you're passionate about transforming how people work and building a lasting company that explores the essence of time itself, we'd love to meet you.
About
the Role As a Senior Analytics Engineer/Data Scientist at Laurel, you’ll turn product and business data into clear, trustworthy insights leaders can act on. You’ll own the analytics lifecycle—from ingestion and modeling to BI visualization and decision enablement. You’ll deliver self-serve insights using SQL/Python and embedded BI (e.g., ThoughtSpot). You will also help define the design patterns and data infrastructure to scale. We’re especially interested in candidates who thrive in early-stage environments and pair analytical rigor with clear storytelling. You’ll partner closely with our CX team to quantify and communicate Laurel’s ROI, and you’ll join customer-facing presentations. You will be able to translate complex methods for non-technical leaders while going deep with technical stakeholders when needed. In addition, you’ll apply machine learning to real-world product and business problems. You should be comfortable prototyping AI/ML models in notebooks, experimenting with approaches (classification, clustering, regression, NLP, etc.), and translating findings into actionable insights for the product and business. What you will do Build analyses & automation (SQL/Python) Run recurring ROI analyses (Business Impact Reports). Write performant SQL and pandas; productionize repeatable jobs (scheduling, alerts, anomaly checks) with orchestration (e.g., Airflow). Define metrics & model the data Own metric definitions (e.g., True Time vs. Released), create reusable SQL Data Models that serve as the analytics source of truth. Partner with CX on customer ROI Quantify and communicate Laurel’s impact, prepare exec-ready materials, and join customer-facing presentations—translating for both non-technical and technical stakeholders. Ship dashboards & actionable insights Deliver customer-facing ThoughtSpot dashboards and turn findings into concise actionable insights Raise data quality & instrumentation Add validation tests and monitoring, triage data issues quickly, and collaborate with Product/Engineering to improve data quality. Data Platform Development Design, build, and maintain Laurel’s Analytics Data Warehouse as the single source of truth for analytics and reporting needs. Create scalable ETL pipelines to ingest, process, and organize data from diverse sources (PMS, Web Analytics, WebApp). Deploy and maintain Business Intelligence tools to provide analytics and reporting capabilities. You will be a great fit if you have Education: Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience. Experience: 3+ years of professional experience as a Data Scientist. Ideal candidates will be comfortable working with large-scale data systems. Technical Proficiency : Advanced SQL and Python Experience with data orchestration tools (e.g., Airflow, Prefect, Dagster). Proficiency in modern data warehouses (e.g., Snowflake, BigQuery, Redshift). Familiarity with data modeling, warehousing principles, and BI tools (e.g., Thoughtspot, PowerBI, Tableau). Ability to build ML models and quickly prototype solutions (classification, clustering, regression, NLP) that inform product direction. Experience With : Cloud platform expertise (AWS, GCP, Azure). Knowledge of dbt, Kubernetes, and Terraform. Exposure to CI/CD pipelines and DevOps practices. Soft Skills : Strong problem-solving and communication skills. Ability to work in a fast-paced startup environment and manage multiple priorities. Nice to haves Experience with knowledge worker productivity tools Familiarity with modern data visualization libraries (e.g., D3.js ) ML and AI: Prototype, build and test ML models to quickly validate hypotheses and generate insights that guide Laurel product features Flexibility and Logistics Location: This role will be hybrid based in our San Francisco office, 3 days per week. We will consider exceptionally qualified candidates based in other US-locations on a case by case basis.