ML Engineer
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Job description
A fast-growing company that is helping real estate organizations dramatically increase the effectiveness of their back-office activities, is looking for an ML Engineer. The engineer will be collaborating with Engineering and Product to obtain additional data or develop enhanced data visualizations to gain a clearer understanding of issues. The company provides technology that assists customers in reducing AP expenses, accelerating AP cycles, and increasing AP productivity by substituting intelligent, automated solutions for labor-intensive, repetitive tasks. Candidates who are keen to assist with data operations and tooling to help our customers comprehend and receive better predictions can take advantage of this fantastic opportunity.
Job Responsibilities:
- Collaborate with Engineering and Product to leverage data to comprehend business customer challenges
- Demonstrate a value-delivery perspective
- Create trials with predetermined results that stakeholders have approved
- Utilize best practices and cutting-edge tools to minimize waste and faults while maintaining a delivery-focused approach
- Experiments and methods to improve prediction quality should be implemented to improve product performance
- Conduct experiments with machine learning approaches and demonstrate or refute their applicability to the performance of the product
- Assist engineering in implementing novel production procedures
- Put strategies into action to enhance model deployment, performance tracking, and further MLOps projects
Job Requirements:
- Bachelor’s/Master’s degree in Engineering, Statistics, Informatics, Information Systems, Computer Science (or equivalent experience)
- At least 4 years of relevant experience as an ML Engineer
- Prolific experience working with Snowflake, Airflow, and AWS
- Strong understanding of ML models and their applications
- Demonstrable experience working with Python, Machine Learning, and Data Science libraries
- Extensive experience working with Data wrangling and associated SQL skillset
- Solid experience with feature extraction and engineering
- Experience overseeing projects from requirement collection to completion
- Previous experience in an early-stage startup and experience scaling data processes
- Strong knowledge of tools and the pros and cons of building versus purchasing in order to solve issues related to coordinating the execution of tasks, deploying models, monitoring performance, etc.
- Prior full-stack machine learning experience and the ability to define problems and carry them out through production deployment
- Some prior expertise with stream-processing systems and creating and managing a data pipeline from scratch is nice to have
- Thorough understanding of the Snowflake/Kafka experience in particular
- Extensive knowledge of data input and orchestration tools (such as Airflow, Airbyte, Flyte, etc.) is desirable
- Strong background using Data Transformation Tooling (DBT) and proficient knowledge of AWS is preferred
- Excellent English communication skills, both spoken and written
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