Masters of Management Analytics (Data Science and Machine Learning), Smith School of Business at Queen’s University. 9 years of combined education and experience in machine learning, data science & analytics, artificial …
Masters of Management Analytics (Data Science and Machine Learning), Smith School of Business at Queen’s University. 9 years of combined education and experience in machine learning, data science & analytics, artificial intelligence, management and decision making. Expert in communicating complex technical topics to various audiences in a clear well-defined format. Excellent problem solving and teamwork skills. Experience in data cleaning, feature engineering, EDA (Visualizations), regression, classification, time series analysis, clustering, Big Data, NLP, descriptive, predictive and prescriptive models and model tuning. Expert in analyzing data and building models to provide insights and recommendations. Proficient in Python, R, SQL, Tableau, Spark, SAS, PowerBi, and Azure Databricks.
● Consult with clients and stakeholders to drive data-driven decision-making,
enhancing operational outcomes and business development.
● Provides expertise on data technology, machine learning models, configuration, and
usability to optimize analytics capabilities within the organization.
● Lead the development of data strategies, offering consultative insights and solutions
to improve processes, efficiency, and foster business growth.
● Collaborate with clients and stakeholders to analyze data, ensuring its quality, and
measuring the impact of data-driven models on organizational performance and
development.
● Spearhead end-to-end development and deployment of machine learning models
for customer segmentation, customer acquisition and experience, and risk
management
Consult with clients and stakeholders to drive data-driven decision-making, enhancing operational outcomes and business development.
Provides expertise on data technology, machine learning models, configuration, and usability to optimize analytics capabilities within the organization.
Lead the development of data strategies, offering consultative insights and solutions to improve processes, efficiency, and foster business growth.
Collaborate with clients and stakeholders to analyze data, ensuring its quality, and measuring the impact of data-driven models on organizational performance and development.
Spearhead end-to-end development and deployment of machine learning models for customer segmentation, customer acquisition and experience, and risk management.
Coordinated and communicated job details to multiple teams, fostering effective collaboration among staff and stakeholders.
Conducted daily inspections on job sites, ensuring compliance with client requirements and maintaining high-quality project standards.
Implemented agile methodologies and project management tools to efficiently manage scheduling and budgeting across multiple projects.
Proactively sourced leads for future projects by leveraging diverse channels, contributing to the organization's sustained growth and success.
Orchestrated ad hoc analyses and reporting for internal stakeholders, ensuring timely and insightful information dissemination.
Implemented an advanced acquisition model with a k-means clustering algorithm, resulting in a substantial 2% improvement in the acquisition rate.
Enhanced client relationships by directly engaging with clients to understand, validate, and document business requirements, boosting transparency in billing, objectives, and KPIs.
Negotiated and established service engagement agreements with clients, defining project timelines, budgets, and quality benchmarks to achieve consistently high customer satisfaction.
Managed and mentored a skilled team in utilizing analytical tools (Tableau, R, Python, SQL), overseeing dashboard creation, query execution, and effective communication of findings, fostering continuous improvement and skill development.
Developed and integrated a MySQL and LUA-based database through the Iguana integration platform to manage information for "unregistered patients," streamlining data storage and retrieval processes.
Conducted in-depth exploratory data analysis on client data, employing data manipulation tools to identify bottlenecks and enhance operational processes, contributing to improved efficiency.
Engineered a two-channel interface utilizing the Iguana integration engine to automate the patient registration process.
This implementation resulted in a noteworthy reduction of four minutes in the average patient registration time, optimizing overall system efficiency.
Conducted comprehensive data collection for cost/risk analysis, optimizing resource allocation and supplier utilization, resulting in a notable 3% reduction in overall costs.
Executed data cleaning and preprocessing tasks, leveraging R and Python to build predictive models for product demand forecasting.
Utilized pricing analytics knowledge, by using logistic models, to determine optimal price points and maximize revenues by examining supply and demand dynamics.
Applied ARIMA and linear regressions for analyzing price elasticity.
The developed model significantly contributed to optimizing price points for Toyota's product portfolio.
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