Quantitative Data Engineer (ML Focus)
Job Location:
Job Location:
New York City, NY - USA
Monthly Salary:
Not Disclosed
Monthly Salary:
Posted on:
30+ days ago
Posted on:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
Quantitative Data Engineer (ML Focus)
Location: New York NY (1NYP Hybrid 3 Days Onsite)
Duration: 12 Months (Potential for Conversion)
Interview: Virtual and final onsite
Job Overview
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.
Required Qualifications (Must-Have)
- 10 years of total experience in Data Engineering / Software Development
- Strong experience in C and Java (legacy model understanding)
- Hands-on experience with:
- Python
- Spark / PySpark
- PyTorch
- Proven experience working with Quantitative / Risk / ML models
- Experience converting Java/C models into Python/PySpark pipelines
- Strong knowledge of:
- Machine Learning & Statistics
- Model development training and inference
- Expertise in MLflow Databricks and Snowflake
- Strong SQL and database programming skills
- Experience with Unix/Linux (Shell/Perl scripting)
- 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
