ML Engineer | 2+ Years Experience | Python, TensorFlow, Pandas
Actively Looking for Work
Professional Background
Machine Learning Engineer with 2+ years of experience bridging telecom, data analytics, and ML. Skilled in designing and evaluating predictive models for network performance and traffic forecasting. Strong in time-series modeling, model comparison, and turning complex data into actionable insights. Proficient in Python, scikit-learn, TensorFlow, Keras, Pandas, and visualization tools like Seaborn and Matplotlib.
Achievements and Projects
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Enhanced 5G network forecasting accuracy by 23% using time-series models (GRU, LSTM, SVM), supporting more efficient network planning and resource allocation.
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Developed a machine learning dashboard to monitor global food security trends using FAO and World Bank datasets, enabling clearer visualization of regional disparities and policy insights.
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Built a social media analytics pipeline that unified data from multiple platforms via REST APIs, applied NLP for sentiment analysis, and provided actionable insights on audience engagement for strategy refinement.
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Delivered multiple end-to-end ML projects leveraging supervised and unsupervised learning, data preprocessing, and model tuning to solve real-world analytical problems in a structured, reproducible manner.
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Communicated ML findings effectively through compelling visuals and reports tailored for technical and non-technical audiences, leading to greater stakeholder alignment and data-driven decision-making.
Core Tech Stack
Enhanced 5G network forecasting accuracy by 23% using time-series models (GRU, LSTM, SVM), supporting more efficient network planning and resource allocation.
Developed a machine learning dashboard to monitor global food security trends using FAO and World Bank datasets, enabling clearer visualization of regional disparities and policy insights.
Built a social media analytics pipeline that unified data from multiple platforms via REST APIs, applied NLP for sentiment analysis, and provided actionable insights on audience engagement for strategy refinement.
Delivered multiple end-to-end ML projects leveraging supervised and unsupervised learning, data preprocessing, and model tuning to solve real-world analytical problems in a structured, reproducible manner.
Communicated ML findings effectively through compelling visuals and reports tailored for technical and non-technical audiences, leading to greater stakeholder alignment and data-driven decision-making.
My core tech stack includes:
- Languages: Python, SQL
- Libraries & Frameworks: scikit-learn, TensorFlow, Keras, Pandas, NumPy, Matplotlib, Seaborn
- Tools: Jupyter, Git, VS Code
- ML Techniques: Time-series modeling, deep learning (RNNs, LSTMs), SVM, data preprocessing, model tuning, evaluation metrics.
Career Goals
I am actively seeking Machine Learning Engineer or Data Scientist roles where I can contribute to impactful, data-driven projects—especially in areas such as time-series forecasting, predictive maintenance, and intelligent operations. I’m passionate about applying machine learning to optimize systems, enhance customer experience, and tackle complex business problems across diverse industries including tech, finance, logistics, and operations.