Worked with a large global research and advisory firm to design and develop a data platform including architecture, prototyping, and development of data extract, transformation, cleansing, and integration of structured and unstructured data.

Developed optimal design of data warehouse environments, analyzing complex distributed data deployments and making recommendations to optimize performance.

Transformed raw data into useful information and insights using analytics, BI, and visualization tools.

Developed and tested T-SQL stored procedures and SQL functions for ETL processing including staging and target processing.

Created and maintained optimal pipeline architecture in Cloud Microsoft Azure using Data Factory and Databricks.

Delivered denormalized data for Power BI consumers for modeling and visualization from the produced layers in the Azure Data Lake.

Used web scraping techniques like Beautiful Soup in Python to extract and organize competitor INGO data.

Worked collaboratively with the board to develop a strategy and approach to defining business challenges to be answered by data analytics.

Managed other data scientists' workload and priorities and developed product/area-specific Data Science roadmaps with increased job efficiency of 45%.

Developed strategies to help clients activate their analytics and create a more data-driven culture.

Supported the development of the Data Analytics team both as line manager and mentor for junior team members.

Measured the effectiveness of improvements through deep analysis of data on performance metrics, striving for high-quality, cost-effective improvements.

Formulated, suggested, and managed data-driven projects to further business interests, tracking and making suggestions for ways to improve KPIs.

Measured the effectiveness of improvements through deep analysis of data on performance metrics, striving for high-quality, cost-effective improvements.

Assisted in the design of various experiments, formulation, discovery of various hypotheses, and training and scoring of existing and potential models.

Identified key performance metrics and benchmarks related to user behavior, user segmentation, and user retention.

Taught Data Science, full-stack development, and cybersecurity-related courses such as Machine Learning, UML, and R/Python programming.

Researched new teaching techniques and strategies and presented findings to other college professors.

Reviewed the current curriculum to check if updates are needed.

Developed intricate algorithms based on deep-dive statistical analysis and predictive data modeling that deepened relationships and strengthened longevity and personalized interaction with customers, leading to a 25% increase in customer satisfaction and 16% increase in sales.

Updated the company's data warehousing techniques, data recall, and segmentation, resulting in a 30% increase in usability for non-technical staff members.

Developed an ETS for data sources used for reporting by the sales, inventory, and marketing departments and modernized the data streamlining processes, reducing redundancy by 25%.

Implemented loss-minimization techniques resulting in a positive overall loss reduction of 10% on monthly revenues.

Achieved a 14% month-on-month increase in revenue applying dynamic pricing techniques.

Located and defined two new process improvement opportunities that increased the company’s revenue by 15%.

Built statistical models using historical data to conduct customer-based pricing and constructed several predictive models such as bad debt and churn models, resulting in a 20% lower churn and 8% lower high-risk debtors.

Developed prediction algorithms using advanced data mining algorithms to classify similar properties together to develop sub-markets dividing each zip code into sub-markets.

Refined personalization algorithms for 400K customers on web and mobile, boosting engagement and time spent on the platform by 25%.

Solved complex business problems using Machine Learning techniques like Regression, Classification, Supervised and Unsupervised Recommenders, increasing team efficiency by 20% and reducing costs by 27%.

Performed market analysis to efficiently achieve business objectives, increasing sales by 34%.

Used web scraping techniques to extract and organize competitor data for evaluation.

Researched and analyzed political systems in various countries and developed and implemented (social) media strategic plans and political proposals for clients.

Used R to create a matrix of political, demographic, and household data to develop a set of predictive models that applied a score to every voter.

Identified voters who would be positively influenced by ads, mailings, social media, and other outreach efforts, which resulted in local political parties winning several municipal and provincial seats.