SQL AI Jobs

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Build and scale the systems that power model and product evaluations across Harvey; run intake, triage, and prioritization for the evaluation request queue, routing capacity to the highest-value coverage gaps; embed evaluation workflows and readiness checkpoints into the product development lifecycle; create the single source of truth for evaluation status, results, history, and launch readiness; turn Expert-designed evaluation methodologies into scalable, repeatable operational processes; manage human data providers and stand up the internal contract-attorney pipeline, ensuring evaluation quality meets legal standards; work with Engineering and Research to improve evaluation tooling, automation, and dashboards; drive evaluation readiness for major product and model launches across geographies and jurisdictions; document and operationalize evaluation governance as complexity increases; help define how Harvey ensures model accuracy, reliability, and trust at global scale.

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As the Applied Data Science & Insights Lead for GTM Intelligence Solutions and Technical Success, you will be responsible for shaping how OpenAI measures, understands, and improves customer adoption across B2B products by building AI/ML-powered intelligence products that integrate various customer and product data into practical operating systems for GTM and Technical Success. You will define and lead the roadmap for GTM Intelligence and Technical Success insight products, build the data science foundation including metrics and models, develop propensity score models, and create predictive and causal models related to customer health, expansion propensity, churn risk, and intervention effectiveness. You will design next-best-action systems, partner with Technical Success leaders to enumerate playbooks and measure outcomes, develop customer segmentation and benchmarking frameworks, and create scalable insight products embedded into field workflows. Additionally, you will build and lead a small team of data scientists and analytics partners, set technical standards, create team operating rhythms, maintain analytical rigor, and collaborate with multiple departments such as Data Engineering and RevOps to improve data foundations.

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Research Engineers at Distyl design and build data systems that support reliable AI workflows across enterprise environments. They develop pipelines for collecting, cleaning, transforming, labeling, and evaluating domain-specific data used by AI systems. They create data quality frameworks to identify issues such as coverage gaps, ambiguity, drift, duplication, leakage, and other failure modes. Engineers build tools and workflows that convert raw customer data into usable context for retrieval, evaluation, reasoning, and execution. They partner with AI Researchers and AI Engineers to understand how data quality impacts system behavior and production outcomes. They develop strategies involving synthetic data, annotation, and feedback loops to improve system performance in situations where real-world data is sparse or noisy. Their work includes analyzing customer workflows and datasets to determine necessary information for AI systems, its sources, and representation. Additionally, they communicate clearly with internal teams and customer stakeholders about data assumptions, limitations, risks, and tradeoffs.

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Build and maintain components of Clarium's master data enrichment pipeline, which classifies and enriches every product flowing through the platform; design and own classification and entity resolution workflows that combine deterministic logic and large language models (LLMs) for production data processing; build and operate evaluation harnesses, label sets, and regression suites to measure and improve pipeline quality; write production-level Python and SQL code; analyze complex datasets using statistics and machine learning to surface actionable insights and inform pipeline improvements; proactively audit data for quality issues, diagnose root causes, and implement fixes.

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Own the implementation and optimization of Harvey's compliance automation tooling to automate workflows across compliance programs; design and build a compliance data layer in Snowflake by ingesting signals from infrastructure, security tools, and SaaS platforms to create a real-time view of control health and audit readiness; develop AI agents and automated pipelines for evidence collection, control testing, and continuous monitoring at scale; partner with Engineering and Security to map technical implementations to compliance controls and maintain a living, accurate control inventory; build reporting layers that translate compliance signals into clear narratives on risk posture and certification status for executive and cross-functional audiences.

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As a Data Scientist at Legora, you will turn data into decisions by sitting close to the business and taking questions end-to-end: shaping the metric, modelling the data in dbt, running analysis, and making recommendations. You will pull in new data sources as needed, leveraging the underlying instrumentation and platform maintained by the data engineering team. Responsibilities include partnering with stakeholders across Product, Finance, GTM, Growth, and other areas to translate ambiguous questions into structured analyses and clear recommendations; defining key metrics, designing experiments or analyses to test them, and measuring impact; conducting deep-dive analyses on business-critical questions and proactively identifying new questions; modeling necessary data in dbt and collaborating with data engineering on scalability; building dashboards and reports that can be used broadly within the company; and helping shape the data team's operations as it scales, including setting standards, tooling, and ways of working.

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Build the analytical foundation to evaluate compensation competitiveness by connecting offer data, band position, acceptance rates, and market benchmarks into a live system that recommends specific adjustments. Develop predictive models and tooling to help managers and recruiters make better and faster decisions, such as a regretted attrition model that flags at-risk employees 90 days in advance. Design and deploy AI agents that draft first-pass recommendations for high-stakes people decisions including compensation, promotion, and hiring, which are then reviewed and adjusted by people leaders. Build recruiting analytics to connect sourcing channels, time-to-hire, first-year performance, and tenure to reallocate recruiting spend and provide weekly insights to recruiting leadership. Analyze organizational effectiveness by examining spans and layers, talent density, and hiring efficiency to identify structural inefficiencies. Partner with finance to transition from spreadsheets to a live workforce model accounting for attrition, hiring velocity, and ramp time by function. Use LLMs and agentic workflows to analyze unstructured people data at scale, including support tickets, exit interviews, performance reviews, and engagement survey responses. Replace recurring reporting cycles with always-on agents that surface insights to leaders as needed. Support high-stakes organizational and talent decisions with rigorous analysis, including executive hiring, retention, and reorganizations.

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The GTM Engineer - Seller Efficiency is responsible for building and owning core sales workflow automations such as book carving, ROE automation, signal detection, and pipeline automation. They partner closely with the Head of Sales and sales leadership to translate business needs into automation requirements and iterate based on feedback. The role involves building tooling that provides the right customer context for GSMs at critical moments like QBRs, renewals, and expansion conversations. The engineer pushes the limits of Clay and extends the platform into new use cases, feeding product teams with innovative ideas and acting as a practitioner evangelist of Clay infrastructure and GTM Engineering. They partner deeply with GTM leaders and frontline teams to understand revenue motions and identify breakpoints. The engineer owns projects end-to-end from discovery and prototyping through rollout, adoption, and iteration, including automating, augmenting, or redesigning workflows using AI, data, and systems design. They design systems that improve speed, visibility, data quality, and execution across the funnel, connect systems through APIs, webhooks, integrations, and automation layers. They measure the performance, adoption, and business impact of built solutions and improve them over time. Additionally, they act as an internal thought partner on the future of AI-native GTM systems.

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Design and build production systems end-to-end across frontend applications, backend services, APIs, data models, and developer tooling. Develop new vertical product capabilities involving document ingestion, workflow orchestration, retrieval, structured outputs, agent frameworks, and human-in-the-loop review flows. Build and operate data-intensive systems, including ETL pipelines, analytical data models, serving layers, and interfaces for querying and exploring large datasets. Work across a modern technical stack that may include Python, TypeScript, SQL, cloud infrastructure, agent orchestration engines, data processing platforms, and analytical databases. Define service boundaries, schema design, and system architecture for new product areas with an emphasis on reliability, extensibility, and speed of iteration. Ship customer-facing features quickly, then harden them for scale through observability, testing, performance tuning, and operational excellence. Partner directly with customers and internal stakeholders to understand domain-specific workflows, map requirements into technical designs, and iterate rapidly on real usage. Build evaluation, monitoring, and feedback systems that measure product quality in production and inform engineering and model decisions. Contribute reusable platform patterns, abstractions, and internal tools so successful solutions can scale across customers and verticals. Help drive technical direction in ambiguous spaces, making strong engineering decisions with incomplete information and a high degree of ownership.

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As an AI Strategist in Healthcare, you will lead and drive technology solutions within the healthcare vertical and manage the go-to-market (GTM) process with healthcare client engagements from initial meetings through deal closure. Responsibilities include diagnosing client problems through an AI perspective, designing data-driven AI strategies to transform operations and improve outcomes, and rapidly building demos and proof-of-concept solutions on the Distillery platform to demonstrate capabilities to prospective healthcare clients. You will work at the intersection of business strategy and AI implementation by bridging the gap between technical AI tools and business outcomes, collaborating closely with healthcare subject matter experts, and taking ownership of the entire pre-sales process including discovery, solution architecture, and feasibility. Additional duties involve researching clients and their industry landscapes to tailor use case materials, feeding insights to account executives to refine pitches, co-designing solutions with engineering, navigating enterprise data constraints to produce reliable AI outcomes, creating compelling AI ROI storylines and decks, developing reusable GTM assets, maintaining pre-sales account documentation, and ensuring formal handoff to expansion teams.