Purpose
The Machine Learning (ML) Engineer is accountable for designing and developing machine learning and deep learning systems. Your responsibilities include data collection, cleansing, and preprocessing, creating machine learning models and retaining systems. With your exceptional skills in statistics, programming, data science and software engineering, we'd like to meet you. Your ultimate goal will be to shape and build efficient self-learning applications and support the foundational Machine Learning infrastructure.
You will devise evaluation strategies, build evaluation sets, run benchmarks, ensure quality monitoring is in place, and set the agenda / prioritization for where to focus our quality iterations. You will be empowered to model & prototype solutions and will either get them implemented in production or work with one of our other ML engineers and Applied Scientists to make sure it gets implemented at the highest standard of quality.
Essential Duties & Responsibilities
- Develop, deploy, and optimize the inference frameworks.
- Research, analyze, develop and test machine learning components necessary for business strategies and roadmap.
- Collaborate with data scientists, software engineers and DevOps to deploy forecasting algorithms into production.
- Implement monitoring systems to track how models are performing.
- Work to continuously improve model performance and debug where necessary.
- Manage the memory and computational footprint of our algorithms.
- Participate or lead discussions with cross-functional teams to understand and collaborate highly complex business objectives and influence solution strategies.
- Incorporates visualization techniques to support the relevant points of the analysis and ease the understanding for less technical audiences.
- Remains informed on current data and analytics trends (ie: Cloud, Data Mining, Python, Neural Networks, Sensor data, IoT, Streaming/NRT data).
- Sees opportunities to continue to learn in the data and analytics space, whether informal (E.g., Coursera, Udemy, Kaggle, Code Up, etc.) or formal (E.g. Certifications or advanced coursework).
Qualifications and Education Requirements
- Bachelor's degree in quantitative analytics field such as Statistics, Mathematics, Engineering, Actuarial Sciences, or other quantitative discipline.
- Experience working with large, dynamic data sets and develop code to ingest, cleanse, and evaluate data.
- Proficiency with deep learning and machine learning algorithms and familiarity with ML frameworks.
- Demonstrates advanced skills in mathematical and statistical techniques and approaches used to drive fact-based decision-making.
- Knowledge and application of data analysis, data visualization, synthesizing information to communicate insights and drive business outcomes.
- Experience with new and emerging data sets, and incorporation (data wrangling, data munging) into new insights.
Preferred Skills
- Sophisticated knowledge of cloud databases/data warehouses (preferably Snowflake and AWS)
- Advanced knowledge of programming languages (Python) in ML environment
- 3+ years leveraging data visualization and BI tools (Tableau Preferred)
- 3+ years performing complex data extraction from multiple, large data sources
- 3+ years performing complex data aggregation, cleaning, and quality checking
- Preferred experience in Personal Lines Auto Insurance
Other Duties
This job description is not designed to cover or contain a comprehensive listing of activities, duties, or responsibilities that are required of the employee for this job. Duties, responsibilities and activities may change at any time with or without notice.
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