Ideally, the Machine Learning Engineers will have deep technical and domain expertise across multiple domains. Examples of our domains include Positive Attribution for Credit/Risk Scores, Decision Science in Lending, Optimization of Payment Streams, and a suite of services for Financial Institutions and FinTechs that enable consumers to transact in refined and innovative ways.
Master's Degree or higher in Data Science, Physics, Econometrics, Computer Science, Mathematics, Statistics or similar field of study required. PhD is preferred.
8+ years commercial machine/deep learning and modeling experience. Combinations of Software/Data Engineering may be considered with sufficient Client experience.
A strong understanding of Natural Language Understanding, Computer Vision, Statistical Modeling, Visualization and advanced Data Science techniques/methods.
Solve problems that are new to the company, the financial industry and to data science.
Focus on the techniques that will lead to solutions; ability to create models is a plus.
Gain insights from text, including non-language tokens.
Ability to perform error analysis of a given model and explain the analysis to others.
Use the thought process of image annotations in text analysis.
Ability to reduce dimensions and explain impact, following the logic of PCA or similar.
Understand which aspects of a model make it work well.
Familiarity with cutting edge data science concepts, algorithms, and products.
Mid- to Senior-level Python coding experience required.
Experience with Kubernetes, Containers, Docker, REST APIs, GraphQL, Event Streams or other delivery mechanisms required.
Excellent written and oral communication skills on technical and non-technical topics.
Senior Level Software Development, Data Engineering or Data Science Programming is highly desired.
Experience with real world applications of discrete, differential, deterministic and probabilistic mathematical structures with applicability to Finance, FinTech, scoring.
Advanced Interpretation and Validation skills including Statistical methods Classical Client, Deep Learning and Transformer Models and/or Attention Mechanisms.
Advanced Data Modeling, Querying, Structuring, Architecture and Design skills.
Exposure to risk evaluation, credit risk modeling.
Additional Finance and FinTech Experience.