The Senior Machine Learning Engineer at Kount has a strong software development background with additional skills to deliver real-time models at scale. They work on a cross-functional team with data scientists, DevOps, and quality assurance engineers to design, build, and deliver products resulting from collaborative AI research. The successful candidate works closely with data scientists to replicate novel feature engineering techniques and generate models that detect fraud in real-time, with DevOps to architect and monitor systems within the Kount codebase, and with QA to thoroughly test and debug products. This is an engineering position that includes research when selecting appropriate technologies and continual learning with new and existing machine learning techniques. Senior engineers collaborate heavily with team members and other teams, but also work independently with little supervision. Kount evaluates millions of transactions a day, each in a fraction of a second, to determine risk for our customers. The Senior Machine Learning Engineer will be on the AI and Machine Learning Team, and report to the Principal Machine Learning Engineer.
KEY RESPONSIBILITIES INCLUDE, BUT ARE NOT LIMITED TO:
- Design platforms and pipelines for training and running machine learning models.
- Understand business objectives and develop models to achieve those objectives along with metrics to track their progress.
- Productionize machine learning models by building performant data transformations, storage, and pipelines.
- Demonstrate effective, respectful, and honest communication when collaborating with colleagues including cross-functional team consisting of QA, Operations, and other team members.
- Apply development and testing best practices and demonstrate excellent software craftsmanship to produce maintainable, scalable, and quality solutions.
- Contribute to all phases of product development and delivery from Analysis & Design all the way through to successful Deployment.
- Demonstrate subject matter expertise and ownership for your team's services.
- Deliver on company initiatives and projects prioritized for your team and support long term technical vision.
- Collaborate with the product team, architects, and others to document features and changes.
- Participate in peer design and code reviews.
- Show determination and discipline in identifying and driving forward improvements and innovations that add value to Kount and move the IT organization forward.
- Elevate the performance of colleagues through training, mentoring, and promoting best practices.
QUALIFICATIONS AND MINIMUM REQUIREMENTS:
- MS in Computer Science, Engineering, Data Science, or equivalent experience.
- Proven ability to lead significant scope, complex projects to completion.
- Deep technical experience and understanding of multiple technology areas.
- Strong software engineering and architecture background in languages such as Python, Java, C/C++, or Go.
- Experience building models using a machine learning framework such as Spark ML, Tensorflow, or scikit-learn/numpy/pandas.
- Experience with RDMS and NoSQL database systems such as MySQL, Postgres, Oracle, HP Vertica, ElasticSearch, Redis, or Aerospike.
- Familiarity with various machine learning algorithms, such regression, random forest, SVM, clustering, etc.
- Interest or experience in information extraction, deep learning, natural language processing, and/or recommender systems.
- Ability to juggle multiple high-level projects simultaneously, including an awareness of business needs and timelines.
- Operate independently and coordinate work with a cross functional team.
- Experience in a Map-Reduce ecosystem such as Hadoop or Spark and associated tools to build scalable machine learning pipelines.
- Experience with machine learning tools such as Tensorflow, Keras, MLFlow, Pytorch, or equivalents.
- AWS technologies such as EC2, EMR, Lambda, DynamoDB, Kinesis, etc.
- Microservice architecture experience, e.g. REST and/or building APIs.
- Experience profiling and tuning code, including pyspark to run on a Spark cluster at peak efficiency.