Applied Data Science & Insights Leader - GTM Intelligence Solutions and Technical Success
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.
Data Scientist
The Data Scientist will train, evaluate, and iterate on machine learning models for customer feedback tasks, contributing to the custom fine-tuning pipelines and running experiments with rigorous documentation. They will build and maintain LLM-powered features including retrieval pipelines, reranking systems, and insight generation with guidance from senior team members. They will contribute to evaluation frameworks by helping build test sets, defining metrics, and assessing model quality across classification, extraction, and generative tasks. The role involves working on semantic search and retrieval, developing a strong understanding of embedding-based approaches and beyond, writing clean, well-tested code, and collaborating with Engineering on model integration, data pipelines, and monitoring. Additionally, the Data Scientist will work with the wider Data Science team to translate business and product requirements into practical ML experiments and solutions and stay updated with relevant research to bring useful ideas into team discussions and experiments.
Lead Data Scientist
As a Lead Data Scientist, you are responsible for setting the technical direction for complex, business-critical projects, balancing trade-offs between speed, innovation, and reliability, designing and implementing reliable, production-grade technical solutions with comprehensive documentation, defining project problems and developing clear roadmaps, overseeing end-to-end delivery across multi-disciplinary workstreams, leading technical scoping and feasibility studies for high-value sales and strategic engagements, managing relationships and communications with demanding clients to foster trust and align technical solutions with long-term commercial goals, driving the adoption of best practices and robust technical processes across the wider Data Science craft, and mentoring and developing other data scientists and team members to contribute to the growth and technical excellence of the organisation.
Senior Data Scientist
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.
Data Scientist
As a Data Scientist, you will analyze a rich array of real-world data to inform our digital twin model of the electric grid, including topography, LIDAR, imagery, vegetation, structural loading, and electrical connectivity. Your work will drive product direction with high visibility, highlight grid expansion opportunities, identify aging and risky infrastructure, and help customers understand where to build and invest. You will model accurate digital twin electric networks from imperfect data using AI, deep learning, and classical ML algorithms, surface meaningful analytics and metrics such as wildfire risk to guide customer buildout of electrical infrastructure, advise the company on data findings to inform strategy, conduct experiments and A/B tests to improve grid modeling, QA and improve predictive models while identifying data issues, craft scalable data pipelines working with various data sources including LiDAR, aerial photography, photogrammetry, and GIS, and mentor others in best practices for model training, data analytics, and building data-driven products.
Data Scientist, People
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.
Data Scientist, Core Experimentation
Drive the statistical direction and technical strategy for OpenAI’s experimentation platform. Design and improve experimentation methodologies used across product and research teams. Build pragmatic solutions to real-world experimentation challenges, balancing rigor with operational simplicity. Improve the reliability and trustworthiness of experiment results, including detection and prevention of bias, logging issues, and data quality failures. Develop scalable analytical systems and pipelines in Python and distributed compute environments. Partner with engineers and product teams to improve experiment design, metric quality, and decision-making practices. Lead investigations into complex experimentation anomalies and measurement failures. Establish best practices for experimentation governance, interpretation, and statistical correctness. Mentor other data scientists and raise the overall technical bar for experimentation and causal inference.
Protection Scientist Engineer, Intelligence and Investigations
As a Protection Scientist Engineer within Integrity and Investigations at OpenAI, you will be responsible for designing and building systems to proactively identify and enforce on abuse on OpenAI’s products. This includes ensuring robust abuse monitoring for new products, sustaining monitoring for existing ones, and prototyping and incubating defense systems against highest risk harms. You will respond to and investigate critical escalations that are not caught by existing safety systems. The role involves scoping and implementing abuse monitoring requirements for new product launches, improving processes to sustain monitoring operations for existing products by developing automation approaches, and maturing systems for detection, review, and enforcement of abuse for major harms. You will work cross-functionally with product, policy, operations, investigative, and engineering teams to understand risks, secure sufficient data, and build scaled tooling. The role includes participation in an on-call rotation for resolving urgent escalations and may involve investigation of sensitive content including sexual, violent, or disturbing material.
Carefull - Data Scientist / AI Engineer
Own end-to-end implementation of AI-driven detection features, from discovery to production deployment and iteration. Design and build data enrichment pipelines to extract structured information from messy, real-world financial transaction data. Research fraud and scam typologies relevant to older adults and translate findings into scalable detection logic. Build evaluation frameworks including metrics, error analysis, and model comparisons to measure system performance and drive improvement. Optimize AI pipelines for accuracy, latency, and cost by making informed tradeoffs on model selection and architecture. Collaborate with Customer Service, Go-to-Market, and partner-facing teams to ensure solutions meet real-world needs and deliver measurable impact. Stay current with developments in LLMs, agent architectures, and applied AI to identify practical applications for the domain.
Lead Data Scientist
As the Lead Data Scientist, you will set the data strategy by defining what is measured, how it is measured, and establishing the metrics architecture that connects product usage, retention, monetization, and growth across the company. You will transform the data team into a product team by building internal data products and self-serve AI interfaces, automated reports, and tools for non-technical stakeholders. You will build the semantic layer, documentation, and context infrastructure to make the data warehouse AI-readable and accurate. Additionally, you will build AI-powered systems and automated pipelines to replace manual work within the data lifecycle, including dbt model generation, data quality monitoring, experiment analysis, and insight delivery. You will own product analytics and experimentation by partnering with Product, Engineering, and Design to design experiments, interpret results, and provide insights that guide product decisions. Your responsibilities also include driving growth and business intelligence by maintaining and evolving dashboards and reporting for Sales, Marketing, Customer Success, and leadership, ensuring metrics are visible, trusted, and actionable. Finally, you will scale the data team’s output through systems and AI-powered tooling to support company growth without increasing headcount linearly.
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