About Longaeva Partners Longaeva: Pronounced "long-AY-vuh", our name is rooted in meaning! We are named after one of the oldest and most resilient living species, the longaeva pine. It represents longevity, adaptability, and persistence. It thrives in extreme conditions and has adapted for thousands of years in challenging environments. We hope to do the same, delivering positive, risk-adjusted returns to our investors, even in dynamic and demanding market conditions.
We are a New York based hedge fund with a global investment mandate focusing on long duration investing across long/short public equities and late-stage private deals. Our founder and CIO, Peter Goodwin, bringing nearly 20 years of investing experience across the healthcare, consumer and TMT sectors.
We are a high-conviction, ideas-driven firm built on collaboration, rigorous primary research, and data-driven decision making. A core part of our approach is leveraging advanced artificial intelligence and technology to enhance our research, generate insights, and drive better investment outcomes. At Longaeva, you'll join a team that values intellectual curiosity, diverse viewpoints, and a shared commitment to uncovering differentiated investment opportunities.
Role Overview Longaeva seeks to accelerate the development and deployment of investment, trading, and risk intelligence capabilities across its multi-strategy platform. In this role, you will embed directly with the CIO to design, prototype, and operationalize tools, automations, and infrastructure that continuously monitor investment opportunities, risk exposures, and process health across the enterprise. The ideal candidate combines strong technical execution, scrappy coding, data pipeline construction, and sharp product intuition, with a proven ability to ship independently and at high velocity.
You will act as the critical bridge between our investment research, data and AI platform and the investment team, translating complex, multi-source data environments into actionable intelligence products that improve decision speed, identify risk, and generate alpha. You will own the full lifecycle: from sourcing and ingesting investment and risk data, to building retrieval and analysis pipelines, to delivering polished intelligence products tailored to PMs, risk officers, and traders.
Key Responsibilities - Build Investment Intelligence Infrastructure: Design and maintain systems that continuously monitor investment, trading, and risk signals across PM, analyst, portfolio, and trading layers, sourced from internal systems, data providers, real-time risk feeds, portfolio construction tools, and analyst research pipelines. Move quickly from idea to working prototype by plugging APIs and data feeds intelligently into investment workflows.
- Own the Data Pipeline: Architect and implement end-to-end data ingestion and staging workflows that centralize structured and unstructured data (real-time feeds, batch files, databases, qualitative commentary) into a queryable environment accessible to Risk, Trading, and Investment teams.
- Enable Intelligent Retrieval & Analysis: Develo_p retrieval and analysis processes that efficiently surface alpha opportunities, limit breaches, liquidity concerns, workflow bottlenecks, overexposures, and other actionable signals, leveraging LLMs, semantic search, and quantitative techniques.
- Deliver Intelligence Products and Identify Alpha-Generating Opportunities: Produce investment, trading, and risk intelligence outputs tailored to distinct audiences (PMs, risk officers, traders), ensuring outputs are actionable, not just informational, and clearly communicate opportunities, risks, and platform health.
- Iterate & Maintain Edge: Capture investment insights and sharpen existing products; track emerging LLM and machine learning techniques, pilot promising ideas, and feed proven wins back into the platform.
Qualifications Education- Degree in Data Science, Computer Science, Statistics, Mathematics, Financial Engineering, or equivalent work experience.
- Academic training in computer science, statistics, and data science techniques, including machine learning methods.
- Preference for candidates with academic training in generative AI techniques and/or quantitative finance.
Experience- At least 3 years of experience scoping, developing, and deploying LLM-based solutions in a cross-functional capacity; demonstrable experience building LLM-based applications is required.
- Hands-on experience building pipelines that integrate real-time and batch data sources across structured and unstructured formats.
- Preference for candidates with prior buy-side, sell-side, or equity research experience, including direct collaboration with portfolio managers, risk officers, or trading desks.
- Experience designing and delivering intelligence or monitoring products for investment or risk stakeholders is a strong plus.
Technical Skills- Highly proficient in Python for data manipulation, statistical modeling, and machine learning; strong SQL skills for database querying and data staging.
- Experience integrating LLMs and AI models into production workflows is required; familiarity with LLM APIs and frameworks such as LangChain and LlamaIndex.
- Experience building data ingestion and orchestration pipelines; familiarity with Airflow or similar orchestration tools is a strong plus.
- Familiarity with vector databases, semantic search, and RAG (retrieval-augmented generation) architectures for querying large, heterogeneous datasets.
- Familiarity with AWS or other cloud platforms and Git for version control.
- Exposure to risk systems, portfolio construction tools, or trading infrastructure is a strong plus.
Soft Skills- Strong problem-solving and analytical skills, with the ability to synthesize complex, multi-source data into clear, actionable outputs.
- Excellent communication and interpersonal skills; able to translate technical findings into formats appropriate for PMs, risk officers, and traders.
- Demonstrated ability to manage multiple workstreams, prioritize under pressure, and meet deadlines in a fast-moving investment environment.
- Ability to work independently and as part of a cross-functional team spanning Investment, Risk, and Trading.
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