Data Architect I

This position is responsible for guiding leaders and teams to make good technology decisions that are innovative, fiscally responsible, and aligned with the overall Technology Strategy at Advantasure, evolving our offerings toward a cloud-native, SaaS-centric Platform.

Work closely with Agile development teams offering guidance, proof of concepts, and technical solutions. Utilize extensive expertise in defining technical solutions, assumptions, constraints, and a quality plan for each opportunity or body of work being proposed (features/initiatives) for a given line of business. Accountable for assigned portions of the architecture output by the development teams.

**ESSENTIAL JOB FUNCTIONS AND RESPONSIBILITIES**:

- Ensure adherence to security and proper data availability.
- Research state of the art machine learning algorithms, patterns, processes, and tooling to identify new opportunities for implementation across the enterprise.
- Developing tools and patterns to implement machine learning and science solutions.
- Enable cross-functional collaboration across the enterprise to implement and standardize science solutions as internal packages, tools, or patterns throughout the company.
- Optimize machine learning processes for performance, accuracy, and software engineering best practices.
- Communicate important technical information clearly to upper management to steer organizational direction, to teammates as part of project work, to other data scientists to guide their work, and to less technical functions such as product management.
- Partner with a wide range of technical personas (i.e., engineering, architecture, data scientists ) to identify and implement best practices around software engineering and analytic procedures.

**SUPERVISORY RESPONSIBILITIES**:
KNOWLEDGE, SKILLS AND ABILITIES:

- Expert in latest ML techniques (e.g., supervised, and unsupervised learning, deep neural nets, reinforcement learning, transformers).
- Expert in coding on ML platforms (e.g. ML Flow, Python)
- Experience with MLFlow and Azure Databricks
- Experience with cloud computing platforms
- Understanding of DevOps and MLOps tool chains and processes, including ML model deployment and accuracy drift detection after deployment in production.
- Experience in supporting data scientists, setting up ML work environments and optimizing data science workflows.
- Experience in a leadership position mentoring junior technical resources and driving towards project deadlines.
- Working experience with tools such as Tensorflow, Pytorch, MxNet or other deep learning libraries. Strong understanding of ML and AI toolkits and processes, such as Kubeflow or MLflow.
- Solid understanding of best practices such as test-driven development and industry established code quality standards.
- Capable of architecting and owning foundational and reusable ML blocks that can accelerate the rest of the team.
- Hands-on experience developing software tools that scale (i.e. Python packages) and using end-to-end tooling to develop, test, and deploy these tools (i.e. CI/CD)
- High level of independence, able to make time-sensitive decisions rapidly and solve urgent problems without escalation.
- Excellent communication skills, particularly on technical topics. Must be able to learn from others and teach others, and to work collaboratively as part of a highly interdependent team.
- Comfort with independent learning of new technologies, and willingness to jump into using unfamiliar tools. - Other data science-adjacent technology experience would be beneficial but is not required, including Docker, Rest APIs, Fast API, Linux and basic shell scripting.
- Strong time and project management skills; the ability to balance multiple simultaneous work items and prioritize as necessary.

**EDUCATION AND TRAINING**:
Bachelor’s degree in computer science or equivalent experience is required.Master’s degree preferred but not mandatory.
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Minimum 10+ years of experience in software engineering or equivalent combination of education and/or experience
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- Strong experience developing architectures which are understandable, robust, extensible, and scalable by utilizing: - hierarchical partitioning (to manage system complexity)
modular / component-based designs (to increase reuse and speed development) - middleware (to promote platform independence)

Documented design patterns (to utilize best practices)
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Experience implementing n-tiered, distributed systems across different HW/OS platforms
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- Experience with iterative, incremental software development processes
- Communicate with Clarity - Be clear, concise and actionable. Be relentlessly constructive. Seek and provide meaningful feedback.
- Act with Urgency - Adopt an agile mentality - frequent iterations, improved speed, resilience. 80/20 rule - better is the enemy of done. Don’t spend hours when minutes are enough.
- Work with Advantsure culture - Best idea wins. Results outweigh effort. Everyone unde