Director - Scalable Solutions Team Lead

2 weeks ago

Hartford, United States The Hartford Full time

Dir Data Engineering - GE06AE

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 Director and Data Engineer within Actuarial to lead machine learning engineers 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. This team is responsible for all modeling that pertains to pricing, class plans, and profitability. The role will lead a team of machine learning engineers to develop MLOps pipelines, develop modeling & data engineering standards and best practices, facilitate implementation of cloud services, and maintain partnership with TDAC(Technology, Data, Analytics, and Cyber) teams.

As a Director and Data 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 will have a Hybrid work arrangement, with the expectation of working in an office location (Hartford, CT; Charlotte, NC; Chicago, IL; Columbus, OH) 3 days a week (Tuesday through Thursday).

**Responsibilities**:

- Actively lead and develop a team of machine learning engineers to deliver and maintain sustainable, reusable, scalable machine learning assets/platforms and production pipelines that assist the modeling\engineering teams in meeting the overall strategic initiatives.
- Develop and implement the design patterns for data pipelines and testing frameworks, both batch and real-time, to meet business needs while balancing maintainability, reliability, security, and scalability.
- Lead the use, development, and adoption of GitHub best practices for version control, documentation, and code collaboration throughout the data science lifecycle and ensure solutions align with best practices.
- Lead the use and adoption of AWS clouds services for modeling and data engineering.
- Collaborate with Data Scientists, Actuaries, Data Engineers, and Product Owners to ensure smooth integration of models into production systems.
- Collaborate closely with business and PM stakeholders in roadmap planning and implementation efforts and ensure technical milestones align with business requirements.
- Drive disciplined innovation by balancing a relentless focus on delivering results and customer adoption with out-of-the-box thinking and a continuous improvement mindset.
- Coordinate activities with cross-functional IT unit stakeholders (e.g., database, operations, telecommunications, technical support, etc.)

Qualifications:

- Bachelor’s or Master’s degree in Computer Science, Engineering, IT, MIS, or a related discipline
- 7+ years experience as ML engineer, architect, engineer, lead data scientist in Big Data ecosystem or any similar distributed or public Cloud platform, with a desire to assume greater responsibilities as a leader and mentor, while still being hands-on.
- 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.
- Experience with leading project execution and driving change to core business processes through the innovative use of quantitative techniques.

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 ra

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