Umesh Rakhe
Data Delivery Head Ai, Ml & Big Data in Financial Services @Citi
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WORK HISTORY
Data Delivery Head Ai, Ml & Big Data in Financial Services @Citi
Belfast, GB
leading data delivery for manual touchpoint repositories and employee productivity metrics, using Python, PySpark, SQL and ML to give leadership an accurate view of how time is spent on core activities.Owning end‑to‑end strategy and execution for the manual touchpoint data platform, ensuring reliable capture and standardization of employee activity data across business units and regions.Leads cross‑functional teams using Python, PySpark and SQL on big‑data infrastructure to engineer clean, scalable datasets for productivity and workforce analytics use cases.Applies machine learning and analytical techniques to classify activities, detect patterns in time usage, and surface opportunities to shift effort toward higher‑value, core work.Partners with senior stakeholders to priorities use cases, define success metrics, and deliver dashboards and self‑service data products that inform headcount, process redesign and automation decisions.Oversees delivery governance, from project planning and risk management to documentation and change management, ensuring initiatives land on time and are adopted by business teams
EDUCATION
Amrawati University
Bachelor of Art (BA)
CMCS
Master of Computer Managment, Information Technology
Cmcs
PG, COMPUTER
Amrawati University
Master, Computer management; Computer Science Duration
SKILLS
ABOUT UMESH RAKHE
Data Science leader with 16+ years in banking, specialising in building scalable data enginnering, ML and data platforms that drive oprational productivity, cost optimisation, and employee analytics.Have led cross‑functional teams (data scientists, data engineers, analysts, governance) to design and deliver production‑grade projects and data products across multiple segments, improving decision quality and operational efficiency.Experience spans oprational risk modelling, collections, oprational productivity, customer segmentation, and personalisation, leveraging big data, cloud platforms, and modern ML frameworks.Partner closely with technology, risk, and bisiness stakeholders to define problem statements, prioritise roadmaps, and ensure pipelines are explainable, compliant, and aligned with regulatory expectations.Passionate about mentoring data talent and shaping data strategy in financial services. Open to connecting with data leaders, researchers, and practitioners in the AI/ML community.Specialties: oprational risk, licensing fraud, credit, collections, customer analytics, model governance, data governance, employee analytics, opration productivity.
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