Job Description
Distributed Squad Lead - Job Description
Role Summary
We are seeking a Squad Lead to manage the recon distributed squad, responsible for driving platform modernisation, technical hygiene, and next-generation reconciliation capabilities.
This role requires strong leadership across delivery, platform stability, and innovation, with a focus on AI/ML adoption, and reducing technical debt. The candidate will play a key role in improving planning discipline, execution predictability, and operational efficiency within the squad.
The ideal candidate brings strong hands-on experience in Python, core ML algorithms, data processing libraries, and CI/CD workflows.
He will own the initiatives end-to-endfrom requirement analysis to production deploymentwhile collaborating closely with cross-functional teams.
Key Responsibilities
- Own end-to-end delivery of squad initiatives across sprints and quarterly roadmap
- Drive structured sprint planning and track progress against planned scope and ensure predictable delivery
- Deliver key initiatives such as Machine learning enhancements, Lead EOL remediation, Platform upgrades (Python, libraries, IM upgrades) & Performance improvements for scalability, and resilience.
- Drive incident management support and Root Cause Analysis (RCA)
- Drive adoption of AI/ML-driven automation capabilities
- Act as the critical interface between business, operations, and technology teams
- Provide structured updates on Delivery progress, Risks and dependencies
- Lead and mentor a cross-functional squad (Developers, QA, Ops SMEs), drive a culture of Ownership and accountability, Continuous improvement, Collaboration across squads
- Support upskilling in modern technologies (AI/ML, platform engineering)
Mandatory Experience
- Experience in Python / Java-based ecosystems
- Proven experience in Agile delivery and squad leadership
- Experience managing Platform upgrades and migrations, technical debt reduction / EOL remediation
- Solid understanding of production support and incident management
- Strong stakeholder management and communication skills
Mandatory Skills
- Strong Python development experience (debugging + writing optimized code)
- Supervised & Unsupervised ML, Classification models, KMeans clustering
- Model evaluation & performance tuning
- NumPy, Pandas, SciPy, scikitlearn, Matplotlib
- Autosys, Git/GitHub, CI/CD (Jenkins)
- Unix/Linux commands, SQL
Good to Have Skills
- Experience in Snowflake, Java/Perl, Azure Cloud, GenAI tools (e.g., Copilot)
- Exposure to AI/ML-driven automation use cases
- Experience with: Observability tools
- Prior experience in financial services / capital markets / operations technology
- Familiarity with large-scale data processing and performance optimization.