Senior Data Scientist
New
RemoteFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 4 years
- Required Skills
- SQLETLMachine LearningData scienceDatabricksNLPMLOpsPySpark
Requirements
- Bachelor’s degree in a STEM field or proven equivalent professional experience.
- 4 years of experience including applied NLP, data labeling, entity or keyword extraction, and related topics.
- Understanding and use of various statistical distributions and use for data modeling.
- Experience with Databricks, PySpark, SQL, model engineering / ML Ops, and associated documentation/formatting.
- Experience working with office productivity software, such as Microsoft Office suite.
- Ability to foster positive business relationships.
- Strong communication skills capable of presenting technical findings to diverse audiences.
- Must be a motivated self-starter who can take direction and execute without constant monitoring.
- Able to travel, as needed, to meet with government customers and stakeholders.
Responsibilities
- Support program initiatives from inception to deployment, ensuring alignment with CBM+ business and mission objectives.
- Lead development and deployment of machine learning models, statistical analyses, and data experiments.
- Own the analytical framework, ensuring robustness, reproducibility, and scalability.
- Partner with domain experts to translate business questions into data-driven insights and products.
- Promote and refine standards for experimental design and analysis.
- Oversee data collection & processing, including implementation/sustainment of ETL pipelines, as well as cleaning and preprocess datasets for usage.
- Perform EDA, to include, performing statistical analysis and visualization to understand data patterns, identifying correlations, trends, and insights to drive product development.
- Lead ML model development using regression, classification, clustering, & deep learning while optimizing models for accuracy, performance, and scalability.
- Collaborate with a multi-functional team to integrate models into applications or APIs.
- Continuously monitor deployed models for performance drift and degradation.
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