Quantitative Data Engineer (ML Focus)
Role details
Job location
Tech stack
Job description
We are seeking a highly skilled Quantitative Data Engineer with strong Machine Learning expertise to modernise legacy statistical risk models. This role focuses on transforming C++/Java-based quantitative models into scalable Python/PySpark-based cloud pipelines, enabling improved performance, scalability, and reduced latency. You will work closely with Quantitative Strategists and Risk Modeling teams to design, build, and deploy next-generation data and ML pipelines in the cloud.
Requirements
Required Qualifications (Must-Have)
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10+ years of total experience in Data Engineering / Software Development
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Strong experience in C++ and Java (legacy model understanding)
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Hands-on experience with:
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Python
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Spark / PySpark
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PyTorch
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Proven experience working with Quantitative / Risk / ML models
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Experience converting Java/C++ models into Python/PySpark pipelines
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Strong knowledge of:
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Machine Learning & Statistics
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Model development, training, and inference
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Expertise in MLflow, Databricks, and Snowflake
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Strong SQL and database programming skills
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Experience with Unix/Linux (Shell/Perl scripting)
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Excellent problem-solving, design, and communication skills
Nice-to-Have Skills
- Interest or exposure to GenAI / Agentic AI
- Experience in Financial Services / Investment Banking domain
- ETL experience with Informatica
- Experience with cloud platforms (e.g., Azure, Snowflake)
- Exposure to Scala, Spark, PyTorch, AngularJS
- Experience with KDB (time-series database)
- Prior experience migrating legacy systems to cloud-native architectures
Benefits & conditions
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$90,000-200,000 per year Vice President, Fixed Income and Equity Data Management Engineer - Full-Stack Engineer At BNY, our culture empowers you to grow and succeed. As a leading global financial service…
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