At PayMe, I lead a team of data professionals as Head of Data. We work collaboratively to leverage the power of data by:Building data-driven products: We utilize a diverse range of machine learning algorithms, including supervised learning (regression, classification) and unsupervised learning techniques, as well as Natural Language Processing (NLP) models. These products empower various business functions at PayMe.Providing comprehensive data analytics support: Our team acts as a strategic partner to all departments at PayMe, including business, sales, marketing, customer support, and product. We translate raw data into actionable insights that drive informed decision-making across the organization.Managing the entire data engineering lifecycle: I oversee the data journey from ingestion to utilization. This includes building robust ETL pipelines to extract, transform, and load data into our data warehouse. Additionally, I ensure optimal data modeling practices for efficient analysis and drive performance optimization to ensure smooth data access and retrieval.

Supporting students in mastering data analytics tools and techniques including Statistics, Python, SQL, data visualization, and machine learning basics. Assist with coding labs, technical projects, and practical exercises. Focus on developing students’ skills in data analysis, manipulation, and visualization for business insights.

Overseeing and leading the following departments:Data ScienceData EngineeringData & Business AnalysisDevelopmentIn this role, I was responsible for:Planning and tracking the execution of company roadmaps.Implementing technological innovations, including evaluating new tools and negotiating with service providers.Developing and implementing ML models using various supervised and unsupervised algorithms.Creating, designing, and managing data pipelines and ETL processes.Maintaining a customer-focused approach.Additionally, the company's technology is focused on the following areas:Developing complex ML models using various supervised and unsupervised algorithms for tasks such as segmentation, sentiment analysis, prediction, causation analysis, and anomaly detection.Integrating augmented AI methodology for improved model accuracy.Resolving business entities through deduplication, record linkage, entity organizational interrelationships, and canonicalization.Collecting public and proprietary data in near real-time, including structured and unstructured data points.

- Data Analysis using Excel, SQL & Python.- Building reports & dashboards using various BI tools.- Implementation of machine learning models.- System setup and configuration using PostgreSQL, Python, Django, Heroku, and native applications.- On-boarding, training, and project management of new and existing clients.