Job Information
The Hartford Senior Machine Learning Engineer in Charlotte, North Carolina
Sr Data Engineer - GE07BE
We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future.
The Hartford is seeking a Senior Machine learning Engineer within Actuarial to design, develop, and implement modern and sustainable MLOps framework to fuel machine learning and artificial intelligence solutions across a wide range of strategic initiatives.
This role will be part of a dedicated hybrid actuarial/data science team designing and delivering powerful analytical tools utilizing statistical modeling, machine learning, cloud computing, and big data platforms to enhance or overhaul core actuarial processes For Commercial Lines this team is responsible for all modeling that pertains to pricing, class plans, and profitability. For Personal Lines this team is responsible for a subset of models used to support Actuarial analytics. The individual will work closely with our data science and data engineering teams to develop training and deployment pipelines for core actuarial models.
As a Senior Machine Learning Engineer, you will participate in the entire software development lifecycle process in support of continuous data delivery, while growing your knowledge of emerging technologies. We use the latest data technologies, software engineering practices, MLOPs, Agile delivery frameworks, and are passionate about building well-architected and innovative solutions that drive business value. This cutting edge and forward focused organization presents the opportunity for collaboration, self-organization within the team, and visibility as we focus on continuous business data delivery.
This role can have a Hybrid or Remote work arrangement. Candidates who live near one of our office locations will have the expectation of working in an office 3 days a week (Tuesday through Thursday) Candidates who do not live near an office will have a remote work arrangement, with the expectation of coming into an office as business needs arise.
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.
Compensation
The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford’s total compensation package for employees. Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition. The annualized base pay range for this role is:
$113,360 - $170,040
Equal Opportunity Employer/Females/Minorities/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age
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Human achievement is at the heart of what we do.
We believe that with the right encouragement and support, people are capable of achieving amazing things.
We put our belief into action by ensuring individuals and businesses are well protected, and by going even further – making an impact in ways that go beyond an insurance policy.
Nearly 19,000 employees use their unique talents in careers that span a variety of disciplines – from developing the latest technology to creating and promoting our products to evaluating future financial risks.
We’re also committed to programs that drive education and support volunteerism, which put human beings first. We do it because it’s the right thing to do, and because when our customers, communities and employees succeed, we all do.
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