Olawale Awe, PhD.

Skilled in

• Business Analytics
• Machine Learning
• Quantitative Methods
• EDA
• Programming (R, Python, SQL, etc)
• Data Analysis and Interpretation
• Reporting
• Cloud Computing
• Training and Research Skills
• Multi-Tasking
• Data Management
• Presentation and Communication Skills
• Ability to work in a diverse team
• Statistical Analysis and Computing
• Biostatistics
• Data Visualization
• Big Data Manipulation/Wrangling
• Collaborative Skills
• Strong Business Acumen

Work

For over a decade, my roles have revolved around the following:

• Formulating, suggesting and managing data driven projects which are geared at futhering business's intrests.
• Collating and cleaning data from various entities.
• Cross-validating models to ensure their generalizability.
• Producing and dissemenating non-technical reports that detail the sucesses and limitations of each project.
• Suggesting ways in which data insights obtained might be used to inform actionable business strategeies.
• Staying informed about developments in Data Science and adajacent fields to ensure that outputs are always relevant.
• Analyzing large, complex datasets to extract insights and decide on the apporiate techniques.
• Selecting features, building and optimizing classfiers using machine learning techniques.
• Data mining using state-of-the-art methods.
• Mentoring, directing and training colleagues on data science best practices.
• Extending customer's data with third party sources of information when needed.
• Using data modelling and evaluation strategy to find patterns and predict unseen instances.
• Enhancing data collection proceedures to include information that is relevant to building analytic systems.
• Performing other duties as directed.

About

I am passionate about data science, research collaboration, statistical learning and capacity building. In recent years, large amounts of data have been generated and collected in various areas of knowledge. These data may come from sensors, GPS signals, satellite images and social media, among many others. Expertise in new statistical and computational strategies to analyze these data appropriately in an acceptable time frame becomes essential in this modern age. Some of the most widely used techniques for data visualization, big data analysis and models for machine learning includes a wide variety of methods for supervised and unsupervised learning related with regression, classification, resampling, regularization, tree based methods, support vector machines, clustering, dimensional reduction, data visualization, forecasting etc. I have expertise in these areas including the ability to extrapolate, analyze and integrate information from high dimensional data sets including functional data and time series analysis, designing machine learning models for pre-clinical and clinical data, data mining and machine learning, multivariate biomarkers discovery and validation for event classification/prediction.

Contact

• Whatsapp
• Email
• LinkedIn
• GitHub

Elements

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