Balyasny Asset Management
Lead Quantitative Data Quality Engineer – Enterprise Data Technology
Balyasny Asset Management, New York, New York, us, 10261
BAM is seeking a hands-on Lead Quantitative Data Quality Engineer to architect and implement real-time, statistical data quality analytics for our enterprise investment data platform. In this high-impact role, you will quantify and communicate the quality of our data, empowering researchers with actionable metrics and insights. You’ll work with hundreds of terabytes of market, reference, and unstructured data, leveraging BAM’s advanced technology and AI ecosystem.
Responsibilities
Design, build, and maintain scalable, real-time data quality analytics solutions for large-scale financial datasets Develop and implement statistical and machine learning models to assess and monitor data quality Collaborate with quant researchers, data engineers, and business stakeholders to define data quality metrics and standards Build tools and dashboards to visualize and communicate data quality insights Lead initiatives to automate anomaly detection and root cause analysis in streaming and batch data pipelines Required Qualifications
Master’s or PhD (preferred) in Mathematics, Statistics, Physics, Computer Science, Finance, or a related quantitative field 3-5+ years of experience as a quantitative developer in investment research Advanced proficiency in Python and SQL; experience with deep learning frameworks (e.g., TensorFlow) Strong knowledge of statistics and statistical analysis Experience with machine learning/AI for data analysis and modeling Extensive experience working with both fixed-frequency and irregular timeseries data at scale Preferred Skills
Systematic investment research background Experience with Rust Experience building streaming solutions or real-time outlier detection systems Experience with cloud data platforms (AWS preferred) CFA certification Personal Attributes
Outstanding attention to detail and creative problem-solving skills Ability to communicate complex technical concepts to both technical and non-technical audiences Results-driven, collaborative, and comfortable in a high-visibility, fast-paced environment
#J-18808-Ljbffr
Design, build, and maintain scalable, real-time data quality analytics solutions for large-scale financial datasets Develop and implement statistical and machine learning models to assess and monitor data quality Collaborate with quant researchers, data engineers, and business stakeholders to define data quality metrics and standards Build tools and dashboards to visualize and communicate data quality insights Lead initiatives to automate anomaly detection and root cause analysis in streaming and batch data pipelines Required Qualifications
Master’s or PhD (preferred) in Mathematics, Statistics, Physics, Computer Science, Finance, or a related quantitative field 3-5+ years of experience as a quantitative developer in investment research Advanced proficiency in Python and SQL; experience with deep learning frameworks (e.g., TensorFlow) Strong knowledge of statistics and statistical analysis Experience with machine learning/AI for data analysis and modeling Extensive experience working with both fixed-frequency and irregular timeseries data at scale Preferred Skills
Systematic investment research background Experience with Rust Experience building streaming solutions or real-time outlier detection systems Experience with cloud data platforms (AWS preferred) CFA certification Personal Attributes
Outstanding attention to detail and creative problem-solving skills Ability to communicate complex technical concepts to both technical and non-technical audiences Results-driven, collaborative, and comfortable in a high-visibility, fast-paced environment
#J-18808-Ljbffr