Machine Learning Engineer - Arizona City

Proficiencies:
Excellent verbal and written communication skills.
Strong knowledge of 1 or more scripting and programming languages (Python, Java, SQL, etc. ).
Background in a variety modeling technique: GBM, logistic regression, clustering.
Experience building, finetune and running inference on transformer models, preferably multi models like LayoutLM.
Ability to create, sanitize and evaluate Apache arrow datasets from relation tables.
Ability to create and monitor metrics like data drift, F1 score to evaluate model performance in production.
Curiosity to try out new models like LLMs on business document corpus.
Ability to think creatively and innovatively.
Ability to react proactivity and sense of timeline urgency.
Good interpersonal skills with the ability to work effectively with individuals and groups at all organizational levels, ability to work independently and as part of a team.
Education and Experience:
MS/PHD degree in a quantitative field: Engineering, math, computer science, statistics etc.
5+ years of experience in business analysis, customer segmentation, and/or predictive modeling, preferably in the financial services industry.
5+ years in using machine learning packages such as Python, R, SAS.
PHYSICAL REQUIREMENTS
This is a stationary position that requires frequent sitting (approximately 95%), repetitive wrist motions, grasping, speaking, listening, close vision, and the ability to adjust focus. It also may require occasional standing, lifting, carrying of 20lbs or less, walking, kneeling, bending/stooping, twisting, pulling/pushing, and reaching above the shoulder. Employees in this position must be physically able to efficiently perform the
Responsibilities:

GENERAL RESPONSIBILITIES:
Regular attendance during business hours and reliable punctuality are essential functions of this position, and conformance with attendance policy is a key responsibility of the employee.
Supports the analytics department by leveraging data technologies to aggregate and structure data, perform statistical analysis, build algorithmic solutions.
Mine and analyze massive amounts of unique internal and external data to gain deep business knowledge and insight on customer activity and usage behaviors and their relationships with sales, payment, credit risks, and other types of behaviors.
Acts as the technical owner of projects that may require significant customization of existing analytic tools, techniques, processes, or development of new ones.
Perform statistical data analysis and understanding, ensure data quality, and develop tracking and reporting systems to determine the effectiveness of models, rules, and other risk initiatives and programs.
Design and create systems to structure, aggregate, and turn petabytes of messy information into statistically significant features for modeling purposes.