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2 months ago

Columbus, United States The Hartford Full time

Responsibilities:

Work closely with Tech leads, Product Manager and Product Owner to deliver MLOPs platform solution in AWS using Python and other tools for the Actuarial community. Work with data engineers/Data Scientist to tackle challenging AIOps problems. Maintain and manage current CI/CD ecosystem and tools. Find ways to automate and continually improve current CI/CD processes and release processes. Help innovate standardize machine learning development practices. Prototype high impact innovations, catering to changing business needs, by leveraging new technologies. Consult with cross-functional stakeholders in the analysis of short and long-range business requirements and recommend innovations which anticipate the future impact of changing business needs. Formulates logical statements of business problems and devises, tests and implements efficient, cost-effective application program solutions. Establish data pipelines guidelines that align to modern software development principles for further analytical consumption. Develop designs that enables real-time modeling solutions to be ingested into front-end systems. Produce code artifacts and documentation using GitHub for reproducible results and hand-off to other data science teams. Qualifications:

Bachelor's in Computer Science, Engineering, IT, MIS, or a related discipline 5+ years experience as ML engineer, architect, engineer, lead data scientist in Big Data ecosystem or any similar distributed or public Cloud platform. 5+ years hands-on experience in integrating, evaluating, deploying, operationalizing ML models at speed and scale, including integration with enterprise applications and APIs. Experience with AWS Services (i.e. S3, EMR, etc). Experience developing with SQL, NoSQL, ElasticSearch, MongoDB, and Spark, Python, PySpark for model development and ML Ops. Expertise in ingesting data from a variety of structures including relational databases, Hadoop/Spark, cloud data sources, XML, JSON. Expertise in ETL concerning metadata management and data validation. Expertise in Unix and Git. Expertise in Automation tools (Autosys, Cron, Airflow, etc.) Experience with Cloud data warehouses, automation, and data pipelines (i.e. Snowflake, Redshift) a plus Able to communicate effectively with both technical and non-technical teams. Able to translate complex technical topics into business solutions and strategies as well as turn business requirements into a technical solution.

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