Data Scientist

Octus•New York, NY

3h•$130,000 - $145,000•Hybrid

About The Position

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 here and our AI framework here.

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience).
2+ 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 tools (e.g., AWS, Docker, GitHub) and an understanding of modern DevOps practices, containerization, orchestration, caching strategies, and cost-aware design.
Strong problem-solving skills and systems thinking, with the ability to balance trade-offs across model quality, scalability, inference latency, cost, and operational complexity.
Ability to interpret and implement research ideas and algorithms, actively contributing to research and development initiatives while translating them into production solutions.
Excellent communication and collaboration skills, with experience working closely with product managers, engineers, and domain experts to deliver actionable technical solutions.
Passion for learning and staying current with the rapidly evolving AI/ML landscape, including emerging best practices for GenAI applications.
Strong ownership and initiative, with the ability to independently drive projects from problem definition to delivery, while being a team player and contributing to the overall success of the data science team.

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. Design, test, and iteratively refine prompts and system instructions using prompt engineering and tuning techniques to improve model reliability, accuracy, and task performance.
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.
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.