Senior ML Data Scientist, Analytics

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 ML Data Scientist, Analytics, you will build the analytical and modeling foundation that enables Laurel’s Product and Engineering teams to make fast, confident, and measurable decisions. This role sits at the intersection of product analytics and applied machine learning, with a strong emphasis on translating AI model performance into real business impact. You will own the full analytics lifecycle: defining product and model success metrics, shaping instrumentation strategies, building canonical datasets, contributing to the feature store, and own evaluation of features. You’ll partner closely with Product and Engineering to embed analytics and ML evaluation into every release, ensuring Laurel understands what about our AI models are working, what isn’t, and why. This is a high-ownership, 0→1 role. You won’t just answer questions. You’ll define the questions, and build the frameworks that allow the company to reason about user behavior, product impact, and model performance at scale. You’ll help operationalize Product Analytics and applied ML as core capabilities of the company. You should be deeply analytical, fluent in SQL and Python, and comfortable shipping production-grade code. You are expected to contribute thoughtfully to our shared analytics and ML codebases, including feature definitions, evaluation logic, and reusable analysis patterns. While this role is not focused on long-horizon ML research, it does require strong applied ML judgment. You should be comfortable prototyping models end-to-end, contributing features to a feature store, and rigorously evaluating models in production settings. This includes understanding and applying concepts such as precision/recall, ROC curves, calibration, clustering evaluation, offline vs. online metrics, and monitoring model behavior over time. You’ll work closely with the AI team to ensure model performance is interpretable, measurable, and clearly connected to business outcomes. What you will do Build Core Product & ML Analytics Define, standardize, and own key product and model success metrics. Build and maintain canonical tables and metric definitions in Laurel’s Analytics Data Warehouse as the trusted source of truth for product and ML evaluation. Contribute to the feature store and ensure features are well-defined, versioned, and measurable. Evaluate and Monitor ML in Production Partner with Product Managers to define success criteria of AI features, guardrails, and evaluation plans before features and models ship. Lead rigorous evaluation of product features and ML-driven functionality: Did it work? For whom? Why? Apply and interpret metrics such as precision/recall, ROC curves, calibration, clustering quality, and offline vs. online performance. Partner with the AI team to monitor model behavior over time and connect model performance to user experience and business outcomes. Ship Actionable Insights Build dashboards, alerts, and self-serve tools that enable teams to quickly understand changes in model performance and how those changes affect users. Proactively surface insights when metrics materially change, rather than reacting to user feedback. Prototype and Develop Applied ML Models Design, prototype, and iterate on applied ML models (e.g., classification, clustering, ranking) to support new product capabilities, improve existing AI features, and inform production model development. This includes feature engineering, establishing baselines, performing error analysis, and partnering with Engineering to productionize successful approaches. You will be a great fit if you have Education: Bachelor's degree in Computer Science, Engineering, Statistics, 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). Experience with Git/GitHub Experience with building and evaluating ML models Familiarity with data modeling, warehousing principles, and BI tools (e.g., Thoughtspot, Mode Analytics). 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 experimentation platforms (LaunchDarkly, in-house frameworks). 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.