Shivam Bhatt

Worked with the Financial Investigative Unit to develop an advanced analytics platform that streamlined the resolution of STRs and SARs. Engineered advanced methodologies for AML/CTF risk scoring, structuring analysis, and shell company detection, leading to a 30% improvement in case triaging efficiency and enhanced identification of high-risk entities.

Designed and implemented a scalable data engineering workflow, enabling efficient ingestion of high-volume datasets. Developed Smart Uploader tool using PySpark to automate data ingestion. Built a robust Data Quality Framework to support risk assessment methodologies - reducing manual effort by 80% and significantly enhancing data reliability across critical decision-making processes.

Led a team of 4 data analysts and modelers, delivering analytical solutions with 100% on-time completion. Oversaw client requirement, task prioritization, and quality assurance to ensure high-impact, data-driven outcomes for client.

Applied advanced statistical techniques, developed custom methodologies and implemented machine learning algorithms to solve complex business use cases—resulting in a 15% improvement in prediction accuracy, 30% reduction in operational inefficiencies, and delivering insights that directly influenced strategic decisions.

Supported all analytics needs including a/b testing, ad-hoc requests, root cause analysis, and custom dashboards to aid business. Worked extensively with SQL and Python to solve use cases.

Developed algorithms for weighted availability, demand forecasting and capacity planning, contributing to a 10% month-over-month increase in platform growth.

Planned growth campaigns by targeting various customer segments' behaviour and key "aha" moments to create a retention framework, resulting in a 15% increase in customer activation.

Partnered with cross-functional teams including product development, operations and marketing to design data-driven solutions, resulting in a 20% improvement in fill rates and a 4% reduction in dump.

Established and maintained data pipelines and infrastructure to ensure that data is accurate, timely and dependable for analysis and decision-making.

Led the analysis of critical e-commerce business metrics and trends, providing data-driven recommendations that improved sales by 15% and maintained over 90% availability.

Analyzed data from SAP, Hyperion, and Rapid Response using SQL and PySpark to build reporting tables for Amgen Pharmaceuticals, addressing supply chain and cost of sales challenges. Contributed to an estimated $2.5M in annual savings through improved forecasting accuracy.

Designed and managed infrastructure and backend workflow for clinical trial web applications using AWS, Aurora, EEA APIs, and Databricks. Developed data pipelines with Airflow.

Led the migration of the DSC project using Databricks, Git, Postgres, and Jenkins to automate deployment workflows—reducing deployment time by 50% and improving release reliability across environments. Developed robust IQ scripts to ensure seamless transitions and system integrity.

Developed common utility functions library utilized across various modules and projects and a Python framework to generate summary dashboards of business rules compliance.

Designed data models, source-to-target mappings (STTM), table interactions, and architectural designs, including both low-level and high-level designs.

Automated document-centric, industry-specific processes using Omnidocs and Newgen's BPM tools, enabling end-to-end process automation and continuous operational improvement.

Built a MySQL-based data warehouse to manage inventory for Maruti retailers, implementing data models and triggers to optimize data structure and automate real-time information updates.

Led a team of 10 to develop data-driven marketing strategies to drive community engagement and event participation, resulting in a 35% increase in outreach effectiveness and participant sign-ups.