Summary
Overview
Work history
Education
Skills
Websites
Custom
Timeline
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Muzammil Firdous

Muzammil Firdous

Sharjah,India

Summary

A performance-driven professional, with 3+ years of experience, accomplishing new summits of achievement through unwavering diligence and leaving an indelible imprint of excellence at every stride, targeting leadership assignments in Data Science and Data Analytics Engineering, with an organization of high repute, preferably in Kochi, Bengaluru, UAE or Remote. Loyal employee with solid understanding of training and mentoring employees. Dedicated team player, proactive and hands-on in task completion.

Overview

6
6
years of professional experience
5
5
years of post-secondary education

Work history

Data Analytics Engineer

Stern & Steller Inc
Chicago, USA
01.2023 - Current
  • Efficiently orchestrated the deployment of an anomaly detection system for a 1000 KM pipeline of HPCL
  • Engineered a real-time anomaly detection system for oil and gas pipelines
  • System is now operational for HPCL
  • Gathered data through API
  • Using Kafka, data from the PIDS was collected which contained the amplitude of vibration occurring along the pipeline
  • Stored JSON responses in an AWS S3 raw bucket
  • Loaded data into AWS Redshift using Facts and Dimension tables in a Star Schema
  • Devised an additional layer in Redshift as a Semantic schema
  • Created aggregated tables and views, if necessary
  • Created Power BI dashboards from the data acquired
  • Developed Python programs to filter noise
  • Established a three-layer rule engine for generating alarms
  • Statistical Analysis: Carried out statistical testing such as ANOVA, Chi-Squre and Hypothetical testing on the features and among features to find relevant features to build analytical models and to find the inter feature dependencies
  • Applied LSTM-based forecasting for detecting anomalies in vibration data for model training and Kafka for streaming real-time data
  • Developed a Convolutional Neural Network (CNN) model for detecting anomalies in pipeline data was received as waterfall chart of Vibrations
  • The model was cross validated and used RandomSearchCV for hyperparameter tuning
  • The CNN model enhances the accuracy of anomaly detection by learning complex patterns and features from the data
  • It was evaluated by finding the accuracy, precision, recall, F1Socre
  • Engaged with clients across India
  • Collected data by conducting real-time simulations.

Founding Member

iShip Innovations Pvt Ltd.
Perumbavoor
01.2021 - 01.2023
  • Developed a model to predict freight charges for lorries across India based on origin, destination, freight weight, and time/date
  • Data Collection & Preprocessing: Gathered and cleaned data; engineered features like geographical routes and traffic patterns
  • Identified key trends and correlations using visualizations (Matplotlib, Seaborn)
  • Statistical Analysis: Carried out statistical testing such as ANOVA, Chi-Squre and Hypothetical testing on the features and among features to find relevant features to build analytical models and to find the inter feature dependencies
  • Model Selection & Tuning: Chose XGBoost for its performance; optimized with GridSearchCV for best accuracy
  • Evaluation: Achieved high accuracy using metrics like MAE and R-squared; outperformed other models (Random Forest, Linear Regression)
  • Deployment: Implemented the model for real-time freight charge prediction aiding logistics planning
  • Founded and successfully ran the startup, managing all operations and growth for 2 years
  • Recruited, trained, and led a team of 6 employees, overseeing daily tasks, long-term projects, and strategic goals
  • Collected and analyzed business data to drive decision-making, enhancing operational efficiency and optimizing resource allocation
  • Managed end-to-end business processes, including product development, customer relations, budgeting, and sales strategies
  • Developed strong leadership and management skills, driving the team to achieve key milestones under challenging conditions
  • Gained practical experience in handling business finances, legal aspects, and stakeholder relations, ensuring sustainable business growth.

Machine Learning Engineer (Freelancer)

Cochin Shipyard Ltd
Kochi
01.2021 - 01.2021
  • Developed a CNN Model for Welding Defect Detection for Cochin Shipyard Ltd., using data preprocessing and augmentation techniques
  • Preprocessed and augmented a large dataset of welding images, applying rotation, scaling, and normalization techniques
  • Implemented a CNN with multiple convolutional and pooling layers for effective feature extraction
  • Trained the model using PyTorch, employing early stopping and model checkpointing to enhance performance
  • Evaluated the model using accuracy, precision, recall, and confusion matrix metrics
  • Deployed the trained model for real-time welding defect detection
  • Developed a Machine Learning Model for EV Battery Degradation in Kochi-Metro Boats
  • Collected and cleaned data on battery usage, including charge cycles, temperature, SoC, DoD, and environmental conditions
  • Engineered features like cumulative energy throughput, operating temperature, and charging patterns to improve model accuracy
  • Applied XGBoost and Random Forest algorithms, optimizing performance with GridSearchCV
  • Evaluated the model using MAE, RMSE, and R-squared metrics; conducted cross-validation for robustness
  • Deployed the model for real-time battery health monitoring, enabling predictive maintenance and extending battery life.

Quality Engineer/AI Project Assist

Alampally Brothers Ltd.
Aluva
01.2019 - 01.2021
  • Delivered actionable insights through residual analysis and coefficient interpretation, leading to significant improvements in manufacturing quality control processes
  • Developed a sophisticated time series forecasting model using SARIMA to predict LPG cylinder demand
  • Enhanced model performance by incorporating external regressors such as economic indicators and weather data, capturing the impact of external factors on demand
  • Applied feature engineering techniques like lag features, rolling statistics, and interaction terms, resulting in a more robust and accurate forecasting model
  • Performed hyperparameter tuning across multiple models to identify the best configuration, ensuring high forecasting accuracy and reliability
  • Deployed the best-performing model to generate precise demand forecasts, optimizing inventory management and production planning processes
  • Developed a predictive model using Linear Regression to estimate LPG cylinder defect rates, incorporating EDA, hypothesis testing, and feature engineering for improved accuracy
  • Implemented feature engineering by creating interaction terms, polynomial features, and transformations, enhancing the model's ability to capture complex relationships
  • Applied PCA for dimensionality reduction, retaining 95% of the variance, which improved model interpretability and reduced multicollinearity
  • Conducted hyperparameter optimization using GridSearchCV, fine-tuning the model parameters to achieve optimal performance with an Adjusted R-squared score of 91%
  • Utilized Matplotlib and Seaborn for data visualization to present key findings and insights, enhancing the decision-making process.

Education

B. Tech - Mechanical Engineering

Kerala Technological University
08.2015 - 05.2019

PG Diploma - Data Science and Engineering

Great Lakes University
08.2023 - 05.2024

Skills

  • Languages & Tools: Python, SQL, NumPy, Pandas, Matplotlib, Seaborn, PyTorch, TensorFlow, Keras, Scikit-learn, Kafka, AWS SageMaker, AWS Lambda, Docker, Kubernetes, API
  • Machine Learning: Deep Learning, Time-Series Forecasting, NLP, LSTM, CNN, Reinforcement Learning, Scikit Learn, PyTorch, Tensorflow, Keras
  • AWS Services: SageMaker, Rekognition, Lambda, S3, Redshift, Kinesis
  • Data Engineering: ETL, Data Warehousing, Data Mining, Feature Engineering, MLOps Pipelines, SnowFlake
  • Data Analysis: Statistical analysis, ANOVA, Chi-Square, T-Test, Randomization, CLT application, Hypothetical testing, EDA, Power BI
  • Problem-solving
  • Communication skills
  • Strategic planning

Custom

Electricity Consumption Forecasting, Developed a real-time AIoT system using AWS SageMaker and Kafka to predict electricity consumption and detect anomalies from IoT sensor data., Statistical Analysis: Carried out statistical testing such as ANOVA, Chi-Squre and Hypothetical testing on the features and among features to find relevant features to build analytical models and to find the inter feature dependencies., Built and deployed LSTM-based forecasting models using SageMaker on electricity consumption predictions., Integrated Kafka for real-time data streaming, enabling dynamic ingestion of electricity usage data and real-time model inference., Implemented an MLOps pipeline using Docker and Kubernetes, automating model updates and deployment in a scalable production environment., Conducted anomaly detection on electricity consumption patterns, enabling proactive monitoring and fault detection in smart grid applications., Tools: Python, Docker, Kubernetes, TensorFlow, LSTM, Power BI

Timeline

PG Diploma - Data Science and Engineering

Great Lakes University
08.2023 - 05.2024

Data Analytics Engineer

Stern & Steller Inc
01.2023 - Current

Founding Member

iShip Innovations Pvt Ltd.
01.2021 - 01.2023

Machine Learning Engineer (Freelancer)

Cochin Shipyard Ltd
01.2021 - 01.2021

Quality Engineer/AI Project Assist

Alampally Brothers Ltd.
01.2019 - 01.2021

B. Tech - Mechanical Engineering

Kerala Technological University
08.2015 - 05.2019

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