Job Description

You are a driven and motivated problem solver ready to pursue meaningful work. You strive to make an impact every day & not only at work, but in your personal life and community too. If that sounds like you, then you've landed in the right place.

Director MLOps Lead

Overview of the Position

The mission for the MLOps / Data Science as a Service core team is to partner with Segment Data Science teams and IT organizations across The Harford to enable deployment of Data Science assets (data, model, business rules) into business workflows in batch and/or real-time mode. The DSaaS team is responsible for creating playbooks and modular code to enable adoption of best practices within the Data Science organization. Learning from recent deployments using existing architectural patterns, the goal is to evolve the playbook to support the cloud adoption on the AWS platform. The team consults on use cases to provide a high level view of cost, time and effort associated with batch vs real-time implementations. The team is responsible for guiding the Data Science practice to allow for faster, cheaper, consistent, and reliable deployments while enabling transparency and reproducibility of modeling assets.

Director of Engineering is a key role responsible for managing a team of Data Engineers, Machine Learning Engineers, and Product Owners to enable on-prem and cloud deployment of model assets into batch and real-time workflows. The three functions supported by the team are:

1) On-prem deployments for claims and small commercial real-time models using established architecture and framework (PO and MLE support)

2) Consulting on new use cases, playbook creation and maintenance, and enabling batch vs real-time decisions

3) Implement an MVP and establish the playbook for MLOps using the strategic architecture patterns defined for AWS cloud platform

This lead will partner with Technology, Business, and Data Science Leadership to ensure reliable, reproducible architecture patterns and standardization of the MLOps pipelines within agile framework. Understanding the Data Science Model Development Process (CRISP-DM), the IT Software Development Lifecycle (SDLC), and AWS architecture patterns for MLOps are essential for this role.

The Director of Machine Learning Engineering is responsible for:

* Managing and mentoring a team of engineers and managers for work that includes POC, implementation, and production deployments
* Creating artifacts for communication with business and executive program sponsors
* Organizing workshops to collect stakeholder input
* Managing and providing program status aligning to the roadmap and milestones for Claims, Small Commercial, and the cloud program. Mentoring Asset Owners and Product Owners on work and deliverables
* Establishing process for peer-review of solutions and scalability and reliability of products
* Setting up the process to create and maintain standard artifacts and playbooks for deployments, including the technology solution and the operational model
* Point of contact for Enterprise Data, Data Science, Business, Cloud Enablement Team, and Enterprise Architecture teams to deliver on the Data Science as a Service strategic roadmap

Experience & Skills

Key Skills

* Previous experience in MLOps required
* Ability to articulate program vision and create both short and long term roadmaps to meet business goals and objectives with input from leadership
* Ability to work and deliver on cross-functional projects
* Ability to work on innovative and new projects with a fail-fast approach to provide optimal solutions that bring the most value to the business
* Articulate cost-benefit analysis and value add of the engineering approach/solution
* Basic understanding of Data Science concepts
* Passion for learning new skills and the ability to adjust priorities on multiple projects based on changing demands/needs
* Strong communication and presentation skills

Minimum Experience

* Software Engineering background with 5+ years of experience in deploying large scale projects (Python/Java, Dockers/containers, Hadoop, Hive)
* Understanding of Data Science/Analytic engineering workflows, 5+ years of hands-on experience with analytic deployments into real-time systems
* 3+ years of management experience
* 3+ years of AWS deployment experience (S3, Snowflake, Python, lambda, ECS/EMR/EKS)
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Equal Opportunity Employer/Females/Minorities/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age

Dir Data Engineering - GE06AE