Senior Data Scientist, Cross-Sector and Thematic Research

Senior Data Scientist 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 We are looking for a Senior Data Scientist to join the Data Science team and support cross-sector, thematic, and high-priority investment research. This is a senior generalist role for someone who can work across sectors, datasets, and research questions to identify where data science and alternative data can create differentiated investment insight. The ideal candidate has meaningful experience working with alternative data in an investment context, strong technical skills in Python and SQL, and the judgment to translate messy data into decision-useful outputs. This person should be comfortable working independently, prioritizing ambiguous requests, and moving between deep dataset work and fast-turnaround research. The role is designed for a senior data science generalist who can support cross-sector themes, select CIO-priority projects, and high-priority coverage gaps across the platform. Sector-specific experience in areas such as Industrials, Consumer, or TMT is extremely helpful, but not required. Key Responsibilities Cross-Sector and Thematic Research Identify and execute analytical research opportunities across sectors, themes, and business models, including select projects tied to CIO priorities. Translate broad investment questions into testable data science workflows and analytical outputs. Communicate results clearly, including limitations, confidence levels, and interpretation risk. Flexible Sector Coverage Data Science Coverage Serve as a senior generalist data science resource across sectors and investment teams. Provide flexible support for high-priority research needs in areas where coverage is limited or stretched. Quickly build context on unfamiliar companies, sectors, datasets, and business models. Triage incoming requests and prioritize work based on potential investment impact, feasibility, and repeatability. Dataset Expertise and Signal Development Develop differentiated signals and analytical frameworks from alternative datasets, including both established data sources and new/emerging datasets. Evaluate dataset quality, coverage, bias, seasonality, leakage risk, and signal durability. Build repeatable tools, screens, and research outputs that can be used across companies, sectors, and themes. Partner with Data Engineering to productionize high-value signals where appropriate. Ideal Candidate Profile 5–10+ years of experience in data science, alternative data, investment research, commercial analytics, or a related analytical role. Prior experience working with investment teams, research teams, or senior business stakeholders. Strong Python and SQL skills. Experience working with large, messy, real-world datasets. Hands-on experience with a variety of alternative datasets. Strong ability to evaluate dataset quality, bias, representativeness, seasonality, and signal robustness. Ability to translate ambiguous investment or business questions into concrete analyses. Strong communication skills and ability to present nuanced findings to senior stakeholders. High judgment, intellectual curiosity, and comfort operating independently in ambiguous environments.