Quantitative Researcher - Alternative Data

Our client, a leading proprietary trading firm, is seeking a Quantitative Researcher to take a senior role within their alternative data research function, with a focus on sourcing, evaluating and extracting tradable signals from non-traditional datasets across global liquid markets. The firm has made significant investments in data infrastructure, large-scale storage and compute, and the successful candidate will have full access to a mature research stack alongside dedicated data engineering and machine learning support.ResponsibilitiesIdentify, evaluate and onboard alternative datasets — including satellite imagery, geolocation and foot traffic, consumer spend, shipping and supply chain, NLP and text, web and app data, weather and sentiment.Lead vendor evaluation and trial processes, including data quality assessment, coverage and survivorship analysis, latency benchmarking and commercial negotiation.Engineer features and derive signals from raw alternative data, with rigorous statistical validation, backtesting and capacity analysis.Translate research signals into production-grade code that integrates with the firm's trading systems and risk infrastructure.Collaborate with traders, portfolio managers and quant developers to refine signals, sizing logic, execution strategy and risk management.Monitor live signal performance, attribute PnL and iterate continuously on dataset and model improvements.Contribute to the broader alternative data research stack — ingestion pipelines, feature libraries, backtesting framework — and share infrastructure improvements across the team.Requirements3+ years of quantitative research experience working with alternative data at a proprietary trading firm, hedge fund or asset manager. Exceptional PhD graduates with directly relevant research will also be considered.Strong Python skills; experience with SQL, distributed data frameworks (Spark, Dask) and cloud platforms (AWS, GCP or Azure).Hands-on experience with statistical and machine learning methods — regression, gradient boosting, time series, NLP, computer vision and deep learning, depending on dataset type.Demonstrated ability to take a dataset from raw form through to a backtested, deployable trading signal.Strong understanding of the markets the signals will trade in — equities, futures, FX, credit or commodities — and the practical constraints of execution, capacity and turnover.Master's or PhD in a quantitative discipline (Mathematics, Statistics, Physics, Computer Science, Engineering, Computational Linguistics or similar).Intellectual honesty, strong communication skills and a pragmatic approach to research.Nice to haveDeep experience in one or more specific data categories: satellite and geospatial, NLP and text, consumer spend, shipping and AIS, web and app data, or sentiment.Background in computer vision, large language models or large-scale NLP pipelines.Experience working with low-quality, noisy or sparse datasets and engineering robust signals from them.