AI Applied Data Scientist Jobs

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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.

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The AI Evaluation Engineer will analyze training and evaluation datasets to identify distributional gaps, labeling inconsistencies, and long-tail opportunities; design and execute labeling campaigns including the development of golden datasets and annotation guidelines; build and maintain dashboards to track model accuracy, regression trends, and product-specific KPIs; investigate failure modes through prompt clustering, error taxonomy development, and user intent classification; operationalize feedback loops by mining product telemetry and human-in-the-loop reviews for signal and translate these into data-driven model improvement strategies; partner with engineers and product managers to run structured A/B tests and human evaluations for new models or features; support the development of scalable data and evaluation infrastructure for LLMs and agents; and work with product, engineering, and legal teams to create clear and transparent processes for handling customer data in AI training, fine-tuning, and evaluation.

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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.

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The Deployment Lead will work closely with Simulation Engineers, Machine Learning Engineers, and customers to understand and define engineering and physics challenges, providing technical leadership to their team. Responsibilities include leading pre-processing and analysis of complex data for predictive modelling, establishing best practices, architecting and developing innovative deep learning models combined with optimisation methods, taking responsibility for the quality and impact of their work and their team's work, designing and testing robust, scalable data pipelines for production environments, leading cross-functional collaboration for seamless integration of data science models with simulations, driving internal R&D and product development to refine models and identify new applications, mentoring junior team members, leading communication and presentations to technical teams and customers, onboarding users and co-developing solutions with customers. The role involves representing the company as a technical authority at customer sites internationally, collaborating on-site to build solutions, influencing technical direction, shaping future solutions and products, and developing leadership skills.

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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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As an Applied Scientist focused on Evaluation & Model Behavior, the responsibilities include designing and implementing systems to measure and improve the performance of Computer Use Agents. This involves the technical definition of model quality through the design of evaluation metrics, curation of training datasets, and engineering system prompts. Responsibilities also include translating product requirements into technical specifications and quantifiable benchmarks, focusing on model behavior design by engineering system prompts and few-shot examples to address capability gaps and behavioral failures, defining evaluation metrics and validating them against human judgment, designing algorithms to filter, score, and select training data, writing Python scripts for data sanitation and management, conducting failure analysis to investigate regressions in model benchmarks and implement fixes, and managing ground truth by defining rubrics and guidelines for human annotation and maintaining reference datasets to establish consistent model performance baselines.

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As a Senior Data Scientist, you will lead project teams delivering bespoke algorithms and high-stakes AI solutions to clients, conceive core data science approaches and design robust software architectures for new engagements, mentor a small number of data scientists and support their professional growth, partner with commercial teams to build client relationships and shape project scope for technical feasibility, contribute to Faculty’s thought leadership through courses, public speaking, or open-source projects, and ensure best practices are followed throughout project lifecycles to guarantee high-quality, impactful delivery.

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The Data Scientist on the Preparedness team is responsible for evaluating and improving mitigation systems including classifiers and detection pipelines across various domains such as biosecurity, cybersecurity, and emerging risk areas. They diagnose false positives and false negatives through deep error analysis, root cause investigation, and make clear recommendations for mitigation adjustments. They build monitoring and measurement frameworks to track the effectiveness of mitigations over time and across user segments and use cases. This role involves identifying trends in over-blocking versus under-blocking, quantifying customer impact, and proposing prioritized interventions. The Data Scientist develops insights from customer feedback, complaints, and usage patterns to detect shifts in adversarial behavior and system failure modes. They expand risk monitoring into new areas including cybersecurity threats and scenarios involving model loss-of-control or sabotage in partnership with domain experts. Finally, they communicate results to technical and executive stakeholders using concise narratives, decision-ready metrics, and clear tradeoffs.

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As a Senior Data Scientist at Faculty, you will lead the design and delivery of AI-powered digital twins tailored for each unique Frontier platform deployment, integrating computational twins into real-world decision processes. Your role includes leading data science efforts within cross-functional teams consisting of engineers, designers, and commercial leads to achieve successful project outcomes. You will analyze core customer challenges to ensure every technical solution offers substantial real-world value, perform rigorous exploratory data analysis, model building, validation, and performance monitoring. Additionally, you will support strong client relationships by collaborating with the commercial team to shape strategic project directions. You are also expected to mentor and develop other data scientists through task leadership and possibly line management.

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The Support Data Scientist will explore large support and product datasets to uncover trends, volume drivers, and user-experience pain points, distilling findings into clear, actionable narratives. They will build, enhance, and maintain self-serve dashboards and reporting tools for non-technical teams. The role involves establishing a unified metrics taxonomy for service-health and performance, and building automated data-sharing pipelines and scorecards with BPO partners. They will leverage LLMs to build bespoke classifiers to automatically label and segment inbound volumes, partner with Data Engineering to ensure reliable pipelines and data quality, and document sources of truth. The role also includes conducting deep-dive analyses and delivering strategic recommendations to leadership, prototyping rapidly with tools like ChatGPT and Jupyter notebooks, and collaborating with Data Science on predictive models and experimentation to translate results into operational recommendations.