Join our team as a senior quantitative analytics associate, where you'll collaborate with business partners to design innovative, automated solutions using cutting-edge technologies, driving operational efficiency in a dynamic, learning-focused environment.       

As a Quant Analytics Associate Senior  within DART (Data, Analytics and Reporting Team), you will play a crucial role in the DART MIS (Management Information System) setup and will be tasked with delivering effective business solutions. You will collaborate closely with various stakeholders and management levels to ensure the delivery of the most optimal solutions. As a member of the DART team you will leverage a broad technology suite to implement automated solutions and deliver data driven insights.

Job Responsibilities

• Own end-to-end MIS solution delivery: requirements gathering, metric definition, source data acquisition, modeling, transformation, validation, and visualization.
• Design and build reliable ELT/ETL pipelines in Python/SQL; implement orchestration, version control, and CI/CD to ensure repeatability and resilience.
• Create executive-ready dashboards and self-service data marts (e.g., Tableau) with intuitive UX and clear metric definitions.
• Apply advanced analytics (forecasting/time series, anomaly detection, segmentation, queuing/capacity planning) to optimize operational performance
• Implement data quality frameworks (unit/integration tests, validation checks, anomaly monitoring), define SLAs/SLOs, and maintain runbooks.
• Translate complex findings into concise narratives for senior stakeholders; influence decisions with data-backed recommendations.
• Identify risks opportunities proactively, and value-unlock levers in operational processes; drive innovation in data management and automation.
• Manage the book of work, prioritize initiatives, and deliver projects on time; lead cross-functional teams as SME and mentor junior colleagues.
• Adhere to data governance, privacy, and control standards; ensure audit readiness, reproducibility, and clear documentation.
   

Required Qualifications, Capabilities, and Skills

• 5+ years of hands-on analytics experience delivering measurable business improvements, with a strong track record in operations analytics or MIS within complex environments.
• Bachelor’s degree in a quantitative or technical field (Economics, Engineering, Physical Sciences, Mathematics, Operations Research, Statistics, Computer Science).
• Expert-level SQL (complex joins, window functions, CTEs, performance tuning) and strong Python (pandas, NumPy; unit testing with pytest; structured logging; packaging).
• Proven experience building automated data pipelines and operating in data lake/cloud environments (Snowflake; AWS services such as S3, Glue, Lambda; or equivalent).
• Strong data visualization experience (Tableau or equivalent), including KPI design, dashboard UX, and audience-specific storytelling.
• Working knowledge of machine learning and applied statistics for operations use cases (time series forecasting, supervised/unsupervised methods, feature engineering).
• Familiarity with data wrangling tools (e.g., Alteryx) and, as applicable, R for statistical analysis.
• Excellent verbal and written communication skills—able to synthesize complex analyses into concise executive narratives and visuals.
• Demonstrated ability to collaborate across functions and levels; influence decisions; and drive adoption of data solutions.

Preferred Qualifications, Capabilities, and Skills

• Banking industry experience and domain knowledge in Consumer & Community Bank (CCB) Operations (e.g., servicing/contact centers, payments/claims, fraud/disputes, collections) and workforce/capacity planning.
• Experience with experimentation and causal methods (A/B testing design, uplift modeling, causal inference frameworks).
• Modern data engineering practices: orchestration (Airflow or equivalent), transformation frameworks (dbt), API integrations, and containerization (Docker) or comparable tooling
• Performance and cost optimization in cloud data platforms; query/profile tuning for large-scale datasets.
• Exposure to risk and control frameworks; model documentation; audit and lineage standards in regulated environments.
• Certifications (e.g., AWS Data/Analytics, Snowflake, Tableau).