
Machine Learning Engineer
2 weeks ago
Job Overview
We are seeking a seasoned Data Scientist to join our team. As a key member of our organization, you will play a vital role in delivering cutting-edge protection and peace of mind to our customers.
- You will apply subject matter expertise in traditional machine learning (ML), large language models (LLMs) and generative AI in the development of ML-driven solutions to protect the digital lives of our customers.
- You will leverage statistical and machine learning knowledge to identify opportunities to improve upon existing protection capabilities using supervised and unsupervised modeling techniques.
- You will use data wrangling skills to clean data and build data pipelines from which to implement model monitoring dashboards and ML models.
- You will apply data mining and exploration skills on raw and cleaned data in a variety of formats to uncover insights for data-driven decision making.
- You will work with stakeholders across the organization to identify opportunities utilizing big data from many different sources.
- You will use data visualization techniques and storytelling skills to convey research results in a meaningful manner with recommendations backed by data.
- You will own data science projects of varying scope from concept to delivery while focusing on delivering a solution to address the original problem statement to achieve success.
- You have worked with and understand LLMs (Large Language Models) and Generative AI.
- You have 5-7 years of experience building high-quality machine learning models.
- Experience in fine-tuning LLMs and classification is a key requirement.
- You possess a Master's or PhD in Statistics, Data Science, Computer Science or other quantitative field.
- You have extensive experience with Python, PySpark, R, SQL and cloud platforms such as AWS, GCP and Databricks.
- You are self-motivated and comfortable working remotely in collaboration with an international team.
- You have strong problem-solving skills that can be applied toward data wrangling, exploratory data analysis, data visualization, model development, model measurement, model deployment and model monitoring.
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