Mr. Shafeeq Ur Rahaman, an Associate Director in Analytics and Data Infrastructure at Monks, boasts over 12 years of extensive expertise in data analytics, cloud solutions, and digital transformation within international enterprises. Since 2020, he has headed the end-to-end analytics function, defining strategy, establishing architectural standards, and overseeing delivery for cloud-native data platforms that support multiple business lines worldwide. He manages the full lifecycle of data products, from batch and streaming ingestion through automated quality checks, metadata governance, and model deployment. A core part of his remit is integrating explainable AI modules, allowing business owners, auditors, and regulators to interrogate model behavior without specialized tooling. Under his leadership, the engineering, data science, and governance teams work to a unified playbook that emphasizes reproducibility, rigorous validation, and continuous risk monitoring.
Before joining Monks, he led modernization programs at Brillio from 2018 to 2020, consulting for Lineage Logistics on serverless data lake design, large-scale ETL orchestration, and interactive dashboarding, which improved supply chain visibility. Earlier, at HCL Technologies between 2011 and 2015, he supported Deutsche Bank’s global trading platforms, optimizing OLTP/OLAP environments and automating Sybase-to-Oracle migrations to enhance performance and deployment reliability. Across these roles, he standardised KPI libraries, automated reporting workflows, and introduced self-service analytics sandboxes—accelerating decision cycles and freeing analyst capacity for advanced insight generation. His holistic approach synchronizes data engineering, statistical modeling, and change management, ensuring analytics capabilities mature in tandem with organizational objectives and compliance requirements.
A committed scholar-practitioner, Mr. Rahaman has authored more than twenty peer-reviewed articles across Elsevier, Springer, and IEEE journals, with research interests in adaptive analytics architectures, plug-and-play AI frameworks, and sustainable automation. His most recent paper, “Quantifying Uncertainty in Economic Policy Predictions: A Bayesian and Monte Carlo Framework” (International Review of Financial Analysis, 2025), proposes a probabilistic model that couples Bayesian hierarchical estimation with Monte Carlo simulation to strengthen fiscal-forecast robustness and scenario planning. He is named on patent filings related to real-time decision systems. He serves the research community as a session chair, programme committee member, and reviewer for leading AI conferences and journals. Beyond academia, he is a frequent contributor to data-science trade publications including Towards Data Science, Data Science Central, and Nightingale and he mentors early-career analysts through professional societies, evaluates international innovation awards, and contributes thought leadership to global media outlets, including Outlook India, AI Time Journal, Vocal Media, and The Inscriber Magazine.
Mr. Rahaman holds a Master of Science in Management Information Systems from the University of Illinois Springfield and a Bachelor of Technology in Electrical and Electronics Engineering from Acharya Nagarjuna University. His professional affiliations include the Fellowship of the Raptors Hackathon, the Royal Society of Arts, and SCRS, as well as Senior Membership in IEEE, ISAC, and INNS. He supports community programmes that promote data literacy, ethical AI practice, and responsible innovation.
