Data Science Platform Engineer

We are looking for an enthusiastic and detail-oriented AI & Data Platform Analyst to join our Platform Engineering team. This is a great opportunity for candidates early in their careers (2–4 years of experience) who are excited to work with cloud-based data platforms and AI/ML technologies, and want to grow in a dynamic, collaborative environment within the financial services industry.

You will support the design, delivery, and enhancement of data infrastructure and analytics solutions using Azure-based tools like Databricks, Data Factory, Data Lake, and Synapse. You'll work closely with platform engineers, data scientists, and business teams to help bring data-driven solutions to life.

Key Responsibilities

1. Platform & Analytics Support

• Assist with the development and enhancement of data platform components using Azure (e.g., Data Lake, Synapse, Data Factory).
• Support data scientists with integrating machine learning models and setting up pipelines for data ingestion, model training, and deployment.

2. AI/ML Operations

• Help monitor and maintain ML pipelines in Azure Databricks.
• Work with engineering teams to ensure models are properly deployed and updated.
• Assist in tracking model performance and help troubleshoot integration issues.

3. Cross-Team Collaboration

• Work with business teams (risk, finance, compliance) to understand data needs and ensure that solutions meet business requirements.
• Participate in regular planning and sprint sessions to track deliverables.

4. Product & Backlog Participation

• Contribute to maintaining the team’s backlog of tasks and enhancements.
• Help document user stories and requirements to guide engineering and data science work.

5. Data Governance & Compliance

• Learn and apply data governance and security practices within Azure cloud environments.
• Support audit and compliance tracking to ensure solutions align with regulatory requirements.

6. Reporting & Metrics

• Help monitor platform performance and usage metrics.
• Assist in generating reports and dashboards that help the team track operational success and model output quality.

Required Skills & Experience

• 2–4 years of experience in data analytics, data science, or platform operations
• Some hands-on experience with Azure tools like Databricks, Data Factory, or Synapse Analytics
• Basic understanding of machine learning models, data pipelines, and how models are trained and deployed
• Experience working with SQL and Python in a data platform or analytics context
• Familiarity with Agile/Scrum teams and understanding of how sprints and backlogs work
• Comfortable working across technical and non-technical teams
• Strong organizational and communication skills

Nice to Have (Not Required):

• Exposure to model lifecycle management tools (e.g., MLflow, Azure ML)
• Interest or background in financial services, especially related to compliance or risk analytics
• Experience with dashboarding or reporting tools (e.g., Power BI, Tableau)
• Familiarity with CI/CD, version control (Git), or Terraform