The company is looking to solve complex business analytical needs by developing and implementing machine learning solutions that provide insight and drive measurable value within the Asset Management vertical.
Requirements
- Deep expertise in Python, data-centric ML techniques including engineering principles for building efficient inference tools
- Experience taking a model from research to production and realizing measurable value from it to the team or firm
- Experience guiding business on identifying AI/ML use cases and optimally contributing to brainstorming sessions
- Enthusiasm for learning new skills and domains, including applying state-of-the-art ML research to real-world data challenges
- Experience or strong interest in quantitative finance and modeling financial markets
Responsibilities
- Leads machine learning projects with diverse scope and complex business and technical challenges.
- Coordinates with senior business and technology partners to develop solutions to the most complex business analytical needs.
- Oversees end-to-end process to push code from research to production.
- Delivers results with clear and measurable impact to the business.
- Consults with senior business and technology partners to identify priorities and establish analytic goals.
- Executes on direction for data identification, collection and qualification activities.
- Presents reports and findings to senior technical and non-technical audiences.
Other
- PhD or Master's in ML, Computer Science, Statistics, Physics, or Finance (with a background in Statistics or ML), with 6+ years plus of industrial experience.
- Ability to work on and drive progress for multiple projects at the same time
- Desires to create a climate that values and rewards contributions, drive, ownership, initiative, and achievement of results
- Excellent planning, project management, leadership, and research skills
- Experience communicating results to business stakeholders with a focus on clear, concise, and understandable delivery including conveying statistical findings through data visualizations
