Wale Aluko

612-***-**** Dallas TX ad2n4k@r.postjobfree.com

Professional Summary

* ***** ********** ** * Data Scientist with a strong background in developing predictive models using Python and TensorFlow. Proficient in data preprocessing, feature engineering, model selection, and evaluation. Skilled in working with large data sets, data visualization, and data analysis. Proven track record of delivering successful machine learning projects, including customer segmentation, churn prediction, and fraud detection. Excellent communication skills, with the ability to convey complex technical concepts to both technical and non-technical stakeholders.

Key strengths:

Programming languages: Proficiency in programming languages such as Python, and R for developing machine learning models and implementing algorithms.

Data analysis: Knowledge of statistics, data manipulation, and data visualization tools such as pandas, numpy, and matplotlib for exploring, cleaning, and preparing data for machine learning models.

Machine learning algorithms: Proper Understanding of machine learning algorithms such as regression, clustering, decision trees, random forests, and neural networks for building predictive models.

Deep learning frameworks: Experience with deep learning frameworks such as TensorFlow, Keras, and PyTorch for developing complex neural network models.

Data analysis and visualization: Able to work with large datasets, extract insights, and present findings in a clear and concise manner using tools like Tableau, PowerBI, or Excel.

SQL queries: Expertise in writing complex SQL queries for data retrieval and analysis, including the use of joins, subqueries, and aggregation functions.

Proficient with Office 365, MS SQL 2016/17, SAP, SSRS, SSIS, SAS, Jira & Service now.

Highly skilled in managing all aspects of all sizes and complexities of multiple projects simultaneously across the software development life cycle phases (SDLC)

Excellent analysis and assessment of business needs and cross-system impacts.

Possess analytical and problem-solving abilities (user support) & writing requirements/user stories using JIRA and Service now and analyzing sprints using Agile (Kanban, scrum, waterfall experience) plus the ability to work under pressure.

Experience and proficiency with various programming languages (e.g., Python), machine learning tools (e.g., sci-kit-learn), statistical packages (e.g., SciPy), SQL/relational databases (e.g., Oracle), and NoSQL databases (e.g., MongoDB, graph database), Linux and shell scripting.

4 years AI’ Experience in leveraging Natural Language Processing (NLP) : Able to work with allowing machines to understand and generate human language more effectively. : Have Knowledge of Models like OpenAI's GPT-3 have demonstrated this with various applications, including chatbots, language translation, and content creation.

7 years’ Experience in leveraging design thinking, client input, and a “data science mindset” to identify, prioritize, and assess opportunities to leverage data science to deliver value to our clients.

Work Experience

Digital Intelligence

Data Scientist November 2019 –Present

Take the database reporting needs and turn them into powerful SQL queries that will extract data and

compile it into meaningful reports using MS SQL 2016 as well as configure the web browsers using HTML. Database), Linux, and shell scripting

Demonstrated reporting skills in Business Objects and Excel, Created Dashboard in MS Excel, VBA, and Macro Working Knowledge.

Managed multiple projects at one time, using Agile methodology and ITIL framework.

Participated in the design and development of business intelligence reporting tools.

Served as the initial contact person for all SQL support queries.

Performed updates to the SQL system as assigned by management.

Improved the experience of software users by soliciting feedback and making software improvements.

Building and publishing customized interactive reports, dashboards, and report scheduling using the Tableau server.

Created action filters, parameters, and calculated sets for preparing dashboards and worksheets in Tableau.

Developed Tableau visualizations and dashboards using Tableau Desktop.

Experience and proficiency with various programming languages (e.g., Python), machine learning tools (e.g., sci-kit-learn), statistical packages (e.g., SciPy), SQL/relational databases (e.g., Oracle), and NoSQL databases (e.g., MongoDB, graph

Experience in Applied data science with design thinking and lean product principles to define, design, and implement algorithms that are core to our business and set the foundation for new products.

Experience in leveraging design thinking, client input, and a “data science mindset” to identify, prioritize, and assess opportunities to leverage data science to deliver value to our clients.

Using machine learning and topic modeling to advise Chief Information Officers (CIOs) on recommended topic areas for engagement.

Identifying opportunities to add business value using data, analytics, and predictive models.

Build tooling and automation to support the platforms.

Manage and investigate platform and infrastructure-related issues.

Troubleshooting skills with deep diving and a proactive approach.

Opportunity analysis and hypothesis generation for stages throughout the end-to-end customer lifecycle

Building advanced analytical models (behavior segmentation, churn prediction, purchase propensity, recommendation engine, etc.) that spans engineering, marketing, and finance.

Design, prototype, implement, and test descriptive, predictive analytics, forecasting, and causal inference models.

Designing and developing machine learning and deep learning systems

Develop machine learning applications according to requirements.

Perform statistical analysis and fine-tuning using test results.

Experienced in text preprocessing in cleaning, tokenizing, and normalizing unstructured text data for use in NLP models.

Expertise in using Knowledge of deep learning frameworks such as TensorFlow, PyTorch, and Keras, and their applications in NLP.

PNC BANK

Dallas Texas

Data Scientist June 2015– October 2019

Determined the software specifications of customers by actively listening to their system needs.

Ran queries, interpret and analyze data and trends in MSSQL Server 2016, and configured web browsers using HTML.

Assist IT service delivery in testing new features and bug fixes as required during the SDLC process.

Provided actionable insights and strategies to the organization by providing background data, analyses, and insights.

Build and implement machine learning models, testing frameworks, and visualizations to make timely decisions and influence the product roadmap.

Provided ad hoc reporting to the team and other internal departments as needed.

Handled common database procedures such as updates, upgrades, backup, recovery, migration, etc.

Used Agile to perform day–day duties and ITIL framework.

Delivered database deployment through the whole system lifecycle using Agile.

Delivered information and analysis to help identify improvements and optimization strategies.

Pulled data from multiple systems and sources for analysis and reporting that drives decisions, and operations, and translates into revenue opportunities and growth.

Working closely with data engineers to identify required data sources, engineer valuable features, operationalize data pipelines, and ensure compliance with data governance and security requirements.

Partnering with data analysts and visualization experts to create products and dashboards that enable stakeholders to easily use data insights and model predictions to make timely business decisions.

seek feedback and development and take ownership of mistakes.

Testing data, features, and models for bias

Worked closely with specific colleagues in functions, geographies, or business units to drive priorities forward.

Applied statistical theory and methods to collect, organize, interpret, and summarize numerical data and use that information to create business solutions and to find effective methods to achieve end goals.

Conceptualize, analyze, build, test, and implement models that utilize various and diverse data sets to solve problems related to employment.

Proactively research data and identify opportunities in the data to drive the product roadmap.

Evaluate data's big picture to solve business problems rather than focusing on metrics alone.

Understand human behavior and know what to look for in the data.

Communicate concisely and persuasively with engineers and product marketers.

Helps identify ways to improve data quality and analysis.

Designs, codes, and tests all sizes of application solutions using object-oriented development, with a current focus on Python.

Resolves complex software issues across multiple software applications.

Provides accurate estimates for solutions; required to be an expert in one application or have breadth across multiple applications.

Designed and implemented machine learning models and algorithms to solve complex problems in various domains such as natural language processing, computer vision, predictive analytics, and recommendation systems.

Collecting, cleaning, and analyzing large datasets to extract insights and features that can be used in machine learning models.

Analyzed scalable, high-performance software systems that can process and analyze large amounts of data in real-time or near-real-time.

Collaborated with cross-functional teams, software engineers, and product managers, to understand business requirements and develop machine-learning solutions that address them.

Education:

BS Project Management Minnesota State University Moorhead, Minnesota

AS Information Technology MnState Technical College Moorhead, Minnesota

Certifications:

Microsoft Certified professional – MTA

ITIL

AGILE (scrum)

Google Cloud Certified

IBM Certified Data Scientist

Certified Analytics Professional (CAP)

Microsoft Certified: Azure AI Engineer Associate

AWS Certified Solutions Architect-Associate

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