Fractional CFO providing strategic financial leadership and support for ambitious start - ups. Driving transformative financial initiatives and managing cash flow across a flexible portfolio.
About the role
- Development Operations Engineer managing Finance Analytics data systems at Webster Bank. Collaborating with data scientists to build ML and statistical model pipelines for production-ready solutions.
Responsibilities
- Develop expert knowledge and experience with Webster’s data systems and tools.
- Design, deploy, and maintain serverless infrastructure and model pipelines.
- Designing, building, and maintaining infrastructure.
- Execute and support CECL Quarterly Production Process and Annual Refresh.
- Build, automate, and monitor statistical and machine learning model workflows from development to production.
- Analyze and organize systems and datasets to derive actionable insights and create efficient and low maintenance pipelines.
- Develop data workflows to support data ingestion, wrangling, transformation, reporting and dashboarding.
- Build and manage CI/CD pipelines to ensure reliable, secure, and repeatable deployments.
- Collaborate across teams to analyze requirements and propose infrastructure or pipeline solutions.
- Use Snowflake for data access and processing, including creating robust data pipelines and integrations.
- Manage data science notebooks in production environments (e.g., SageMaker Studio,JupyterHub).
- Use Git for version control and workflow management across codebases and projects.
- Collaborate with cross-functional teams to understand data requirements and implement effective solutions.
Requirements
- 5+ years of experience working in data engineering and/or DevOps specializing in AI and Machine Learning deployment.
- Experience working with complex data structures within a RDMS (Oracle, SQL).
- Experience in core programming languages and data science packages (Python, Keras, Tensorflow, PyTorch, Pandas, Scikit-learn, Jupyter, etc.)
- Proficient in Python/SAS Programming Language.
- Experience with traditional ML and deep learning techniques (CNNs, RNNs, LSTMs, GANs), model tuning, and validation of developed algorithms.
- Familiarity with commercial & consumer banking products, operations, and processes, or risk & finance background/experience.
- 5+ years of experience leveraging cloud services and capabilities of computing platforms (e.g., AWS SageMaker, S3, EC2, Redshift, Athena, Glue, Lambda, etc. or Azure/GCP equivalent).
- Experience in Reporting and Dashboarding tools (e.g.- Tableau, Qlik Sense).
- Extensive experience with design, coding, and testing patterns as well as engineering software platforms and large-scale data infrastructures.
- Experience in DevOps and leveraging CI/CD services: Airflow, GitLab, Terraform, Jenkins, etc.
- Experience with Data Science project implementation.
- Experience in documenting processes, scripts, memos clearly for internal knowledge sharing and audits
- Strong analytical and problem-solving skills and ability to work in a collaborative team environment.
- Excellent communication skills to convey complex technical concepts to non-technical stakeholders.
- Ingenuity, analytical thinking, resourceful, persistent, pragmatic, motivated and socially intelligent.
- Time management skills are needed to prioritize multiple tasks.
Benefits
- Robust development opportunities
- Meaningful work
- Incentive compensation
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