Overview
Full Time
Skills
Unstructured Data
Analytics
Collaboration
Quantitative Analysis
Build Tools
Dashboard
Data Quality
Root Cause Analysis
Mathematics
Physics
Computer Science
Finance
Python
SQL
Deep Learning
TensorFlow
Statistics
Machine Learning (ML)
Artificial Intelligence
Data Analysis
Modeling
Time Series
Research
Rust
Streaming
Real-time
Cloud Computing
Amazon Web Services
Control Flow Analysis
Attention To Detail
Creative Problem Solving
Job Details
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
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
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