Muhammad Sohaib

Designed and developed comprehensive Data Science and ML pipelines for clients, encompassing data preprocessing, feature engineering, model selection, and hyperparameter tuning.

Built robust predictive models for classification, regression, and forecasting using scikit-learn and advanced statistical techniques, driving data-driven decision-making for clients.

Created automated Exploratory Data Analysis (EDA) reports and interactive dashboards, providing clear, actionable insights for non-technical stakeholders.

Performed data storytelling through dynamic visualizations, KPI design, correlation analysis, and hypothesis testing to extract critical business insights for clients.

Executed end-to-end data science projects, including data wrangling, feature engineering, exploratory data analysis, and Machine Learning model development.

Designed and built automated data pipelines for ingesting, transforming, and loading data from multiple sources into centralized data warehouses, enhancing data accessibility.

Developed Data Science Web Applications enabling in-depth Statistical Analytics, A/B Testing, Forecasting, and Machine Learning functionalities.

Collaborated closely with the team and line manager to analyze complex datasets and generate data-driven recommendations for critical real-world decision-making.

Designed and implemented data warehousing solutions using SQL and PostgreSQL, focusing on schema design, indexing, and performance optimization to enhance data retrieval efficiency.

Applied expertise in Python, SQL, Apache Spark, Databricks, and Hadoop to process large-scale data, understand distributed computing, and orchestrate complex data pipelines.

Managed structured and semi-structured data, performing transformations, normalization, and data quality checks to ensure data integrity and readiness for analytics.