Senior Data Scientist New York, New York, United States

Octus is a leading global provider of credit intelligence, data, and analytics. Since 2013, tens of thousands of professionals across hedge fund, investment banking, management consulting, and law firm verticals have come to rely on Octus to make better, faster, and more confident decisions in pace with the fast‑moving credit markets. For more information, visit:

Working at Octus

Octus hires growth‑minded innovators and trailblazers across the globe to drive our business and culture. Our core values—Action Oriented, Customer First Mindset, Effective Team Players, and Driven to Excel—define an organizational ethos that’s as high‑performing as it is human. Among other perks, Octus employees enjoy competitive health benefits, matched 401k and pension plans, PTO, generous parental leave, gym subsidies, educational reimbursements for career development, recognition programs, pet‑friendly offices (US only), and much more.

Role

Octus delivers breaking news and market‑moving intelligence through cutting‑edge data and technology for hedge funds, investment banks, and law firms—and we’re transforming how professionals access complex and opaque information. As part of our high‑performing AI Innovation team, you’ll help design, build, and productionize modern GenAI and LLM‑powered systems that support both client‑facing features and internal operational efficiency. You’ll work end to end—from shaping ambiguous problems into scalable solutions to deploying reliable AI models in production—collaborating closely with product, engineering, and infrastructure teams. This is a hybrid role based in NYC (3 days in office per week). Curious about what we’re building? Check out our flagship GenAI product, CreditAI , and our AI framework.

Responsibilities Apply strong problem‑solving and critical‑thinking skills to break down complex, ambiguous requirements into clear, implementable technical components and system designs. Design, build, and maintain AI‑powered and data‑driven systems with a focus on modern language and multimodal models, including LLM‑driven applications, RAG pipelines, and agentic workflows. Evaluate and productionize commercial and open‑source LLMs, choosing appropriate models, tools, and techniques for each use case. Develop multi‑step agentic workflows that incorporate tools, external data sources, memory, and control logic. Manage the orchestration of production LLM workflows and agentic systems, ensuring reliability and efficiency through prompt routing, state management, retries, fallbacks, and error handling. Define and implement evaluation and observability frameworks for AI systems, including automated testing, task‑specific benchmarks, regression testing for prompts, human‑in‑the‑loop validation, and performance monitoring. Build and integrate AI models into backend systems and APIs to support both real‑time and batch inference, ensuring solutions are production‑ready, scalable, and efficient. Apply NLP and ML techniques to tasks such as information extraction, semantic search and retrieval, text classification, summarization, and reasoning over text and documents. Collaborate closely with engineering and infrastructure teams to deploy solutions using containerized and cloud‑based environments (e.g., GitHub, Docker, AWS), applying modern deployment and infrastructure practices. Collaborate with product managers, business stakeholders, and domain experts to translate complex, ambiguous business problems into actionable technical solutions, and communicate progress, trade‑offs, and outcomes to relevant stakeholders. Continuously learn and adapt to advancements in NLP and Generative AI to ensure solutions remain innovative and effective. Maintain production‑grade code and services with automated monitoring and performance tracking, using metrics and alerts to guide continuous improvements in models, prompts, and pipelines. Apply systems thinking to design and optimize AI and LLM systems, balancing quality, scalability, latency, cost, and operational complexity, while implementing efficiency improvements using model selection, prompt design, batching, caching, and retrieval strategies. Requirements Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience). 3+ years of experience as a Data Scientist, Machine Learning Engineer, or applied AI practitioner, with a strong foundation in computer science, algorithms, and software development. Advanced programming skills in Python, with experience building production‑grade systems beyond research or experimentation. Solid understanding of machine learning and applied AI concepts, with experience taking solutions from prototype to production. Hands‑on experience designing, building, and deploying LLM‑driven or GenAI applications, including familiarity with vector databases, embeddings pipelines, or semantic search systems. Practical experience with cloud‑based deployments and infrastructure