Machine Learning Developer/Python
Harris Allied · NY · Hybrid
Weak hiring record · median 573 days open
All reasons
- Open 984 days
- 100% of this employer's openings are over 60 days old
In its favour
- Still listed on the employer's own board
Global financial services leader has an immediate need for a Machine Learning Developer to join their growing team in a full-time, hybrid role based in Manhattan. The person in this role will creatively apply their knowledge of machine learning and software engineering to design, build, and maintain high-performance financial data systems for institutional trading teams and asset managers. The Machine Learning Developer will be responsible for designing, implementing, testing, and documenting systems from concept through validation. Excellent hands-on Python coding skills with either Java or C++ are required.
Skills and Experience
- Successful candidates will have a strong knowledge of software engineering and open-source machine-learning environments.
- Excellent Python programming skills with 3+ years of software engineering experience in a production environment.
- Excellent analytical and quantitative skills with a demonstrated ability to conduct research using large data sets.
- Financial services knowledge across asset classes is highly preferred.
- A professional track record of building and applying high-performance machine learning systems for the capital markets industry.
- Experience leading software initiatives for industry-leading capital markets companies is highly desired
- MS or PhD in Computer Science, Machine Learning, Statistics, or a related field.
Responsibilities Include
- Design, develop, implement, test, and document all stages of the data pipeline from prediction to model construction and validation.
- Partner with researchers and internal stakeholders to deliver highly productive end-to-end systems.
- Hands-on coding using Python and Java.
- Build and deliver high-performance machine learning systems using large-scale, financial data repositories.
- The ability to work in a hands-on, collaborative environment with internal stakeholders including technology teams, institutional asset managers, Quant Analytics, and more is required.
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