Applied Data Scientist, Evaluation & Model Behavior
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.
Senior Data Scientist
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.
Data Scientist, Preparedness
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.
Data Science Manager, Integrity
Lead and scale a high-impact Integrity Data Science team by hiring, coaching, and developing data science individual contributors and potentially future managers while setting a strong technical and cultural bar. Drive strategy across multiple Integrity domains including policy enforcement, bot detection, fraud prevention, intellectual property theft, risk measurement, and abuse prevention, balancing near-term response with durable systems. Build and institutionalize analytical rigor through clear metric frameworks, experimentation standards, monitoring and alerting systems, and repeatable evaluation approaches for Integrity interventions. Partner closely with Product and Engineering to shape roadmaps, prioritize projects, and translate ambiguous risk signals into practical product and platform decisions. Evolve team structure and operating model as the organization scales by defining ownership boundaries, improving processes, and creating leverage through better tooling and AI-assisted workflows. Enable cross-organization outcomes by supporting partners outside the Integrity team where integrity risks intersect with product and business goals. Communicate clearly with senior leadership to synthesize complex tradeoffs, surface risks, and drive alignment on priorities and success metrics. Push the team toward an AI-leveraged operating mode using modern tooling and model capabilities to accelerate detection, triage, analysis, and iteration.
Senior Data Scientist, Marketing
The Senior Marketing Data Scientist will partner closely with Harvey’s Marketing organization to build the marketing data science function from the ground up. Responsibilities include embedding deeply with the Marketing organization as a trusted partner to identify opportunities to improve performance and drive growth, defining, tracking, and evolving core metrics across marketing and business functions, and building scalable dashboards and reporting frameworks that enable data-driven decision-making. The role involves designing, implementing, and evaluating models such as multi-touch attribution, marketing mix modeling, and incrementality for comprehensive Marketing Channel and Campaign performance and contribution. The Senior Data Scientist will apply statistical and machine learning techniques to model user behavior, forecast trends, and identify opportunities for growth and optimization. They will translate complex analyses into compelling stories with clear recommendations for cross-functional partners and executives, partner with Marketing, RevOps, and GTM Systems to co-develop data infrastructure ensuring robust pipelines, reliable data sources, and scalable systems to power analytics and modeling. The role also includes leading cross-functional analytics initiatives to synthesize competitive dynamics, customer feedback, and market trends into actionable business opportunities and championing a data-informed culture by establishing best practices, mentoring peers, and shaping the strategic role of data science at Harvey.
Data Scientist - Manufacturing Data (KR)
Design and implement customized AI solutions for manufacturing; analyze manufacturing data to uncover opportunities and develop AI models; collaborate with customers to understand their requirements and deliver clear, data-driven solutions; work closely with internal teams to ensure solutions are feasible, scalable, and aligned with product strategy; present findings to both technical and non-technical stakeholders.
Senior Forward Deployed Data Scientist/Engineer
As a Production AI Ops Lead, you will design and develop the production lifecycle of full-stack AI applications, support end-to-end system reliability, real-time inference observability, sovereign data orchestration, high-security software integration, and the resilient cloud infrastructure required for international government partners. You will take full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies, oversee the end-to-end health of the platform ensuring seamless integration between the AI core and all full-stack components, build automated systems to monitor model performance and data drift across geographically dispersed environments, manage the technical lifecycle within diverse regulatory frameworks, lead the response for production issues in mission-critical environments ensuring rapid resolution and prevention of future issues, translate deep technical performance metrics into clear insights for senior international government officials, and partner with Engineering and ML teams to ensure field lessons influence the technical architecture and decisions of future use cases.
IT Support Specialist
Collaborate with Product teams to understand business objectives and challenges, translating them into data-driven insights and recommendations. Develop and implement predictive models, analytical tools, and methodologies to analyze product usage and customer behaviors. Analyze large datasets to generate actionable insights that guide the development and optimization of engagement and monetization strategies. Design and execute experiments to test hypotheses and measure the effectiveness of various features, product experiences, and strategies. Partner with cross-functional teams, including Product Management and Engineering, to integrate data-driven insights into products and services, driving continuous improvement and innovation. Present findings and recommendations to key stakeholders, including executives, to inform strategic decision-making and shape the company’s Product roadmaps. Collaborate with Data Engineers to ensure data quality, accessibility, and reliability for analysis purposes. Stay current with industry trends, emerging technologies, and best practices in data science, machine learning, and AI to drive innovation and competitiveness.
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