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Roles & Responsibilities:
· Ownership of design, implementation, and deployment of machine learning algorithms in a modern Python-based cloud architecture
· Design or enhance ML workflows for data ingestion, model design, model inference, and scoring
· Oversight on team project execution and delivery
· Establish peer review guidelines for high-quality coding to help develop junior team members’ skill set growth, cross-training, and team efficiencies.
· Visualize and publish model performance results and insights to internal and external audiences.
· You will collaborate with executive-level stakeholders to understand their strategic objectives and identify opportunities to leverage data and data quality for enabling AI and machine learning.
· You will be responsible for the overall AI system design, problem framing and governance, identifying and managing risks and issues.
· Master’s in a relevant quantitative, applied field (Statistics, Computer Science, IT, Engineering)
· 5 years of work experience in the end-to-end lifecycle of ML model development and deployment into production within a cloud infrastructure
· You have experience in leading the analysis and modeling of different datasets and overseeing the development, deployment, and productionization of the models.
· You have experience with statistical modeling, machine learning, deep learning, optimization, and other data science techniques to implement end-to-end data science projects.
· Experience on the Databricks platform is highly preferred.
· You evaluate model performance using appropriate metrics and validate models to ensure robustness and reliability.
· Proficiency in Python and SQL required; PySpark experience a plus
· Working knowledge of MLOps
Data Scientist
Software Engineer
AI Engineer
Machine Learning Engineer
MLOps Engineer
Python - 3 years
SQL - 3 years
Machine Learning - 5 years
Deep Learning - 3 years
Statistical Modeling - 3 years
Databricks - 2 years
PySpark - 2 years
MLOPS - 2 years
Data Science - 5 years