Data Scientist, Systematic Data Science - New York

rule-based checks.Enhance Python-based data quality checks as part of the onboarding process.Author and maintain comprehensive documentation for data sources, business logic, and QC processes.Provide hands-on support to PMs and analysts in understanding, exploring, and leveraging new datasets.Lead evaluation of new datasets, including exploratory data analysis, feature engineering, and assessment of data suitability for systematic strategies.Collaborate with PMs to design and execute signal research projects, including hypothesis generation, backtesting, and statistical validation.Develop tools and frameworks to accelerate dataset evaluation and signal discovery, enabling PMs to rapidly assess the value of new data sources.Serve as a subject matter expert (SME) for data-related questions, providing recommendations on data sources, structure, and usage.Work closely with Data Engineering to ensure seamless data integration, transformation, and delivery.Provide feedback and requirements to

improve data pipelines, infrastructure, and automation.Participate in the design, creation, and maintenance of datasets used firm wide.QualificationsMaster’s, or Ph.D. in Computer Science, Mathematics, Physics, Statistics, Financial Engineering, or a related quantitative field.5+ years of relevant experience as a data scientist/analyst in financial services.Demonstrable experience building data collection, cleansing, and delivery infrastructure.Strong programming skills in Python and SQL; experience with data manipulation libraries (e.g., pandas, NumPy) and Database.Experience with data quality assessment, validation, and documentation.Experience supporting research teams or PMs in dataset evaluation, exploratory data analysis, and signal research.Familiarity with cloud data platforms (AWS S3, Redshift, Snowflake) and workflow orchestration tools (Airflow, Git, Jira).Experience analyzing financial data across asset classes (FX, commodities, fixed income, equity).Experience with vendor data (reference, fundamental, market, alternative) and multi-vendor aggregation.Experience with statistical modeling, machine learning, and building predictive models on financial data.Excellent communication skills, with the ability to translate business requirements into technical solutions.Strong problem-solving skills, intellectual curiosity, and attention to detail.