A Minail

A Minail

Principal Machine Learning Engineer
United States of America

About Me

Highly accomplished Data Scientist and AI Engineer with over 10 years of experience in developing, deploying, and optimizing advanced machine learning models and AI solutions across diverse industries, including finance,…

Experience

Lead Data Scientist & Machine Learning Engineer

self employed
Mar 2021 - Present · 5 years 7 months

Tailored travel suggestions for users based on
personalized preferences
Employed RNNs and LSTMs to predict upcoming
popular travel destinations using historical user
behavior
Designed and implemented data cleaning and
wrangling processes, improving data quality and reliability

Lead Data Scientist & Machine Learning Engineer

Self-Employed
Mar 2021 - Present · 5 years 7 months

<p>Developed an AI-powered recommendation engine using TensorFlow and scikit-learn<br>
Tailored travel suggestions for users based on personalized preferences<br>
Employed RNNs and LSTMs to predict upcoming popular travel destinations using historical user behavior<br>
Fine-tuned search functionality with Apache Spark, optimizing itinerary planning<br>
Integrated a chatbot with NLP capabilities for immediate user support and inquiries<br>
Efficiently managed extensive travel data using PostgreSQL<br>
Optimized data retrieval with Apache Kafka<br>
Executed cloud-based solutions on AWS, leveraging EC2 and Lambda for seamless scalability<br>
Elevated user experience with interactive data visualizations using Seaborn and Plotly<br>
Applied gradient descent and evolutionary algorithms to refine travel route suggestions<br>
Implemented CNNs for automatic categorization</p>

Senior AI Engineer

North Bay Solutions
Jul 2018 - Feb 2021 · 2 years 7 months

<p>Led a team to build predictive models for industrial process optimization using Python and R<br>
Developed time series forecasting models (ARIMA, LSTM) for demand prediction in the energy sector<br>
Designed and implemented data cleaning and wrangling processes, improving data quality and reliability<br>
Created interactive dashboards using Tableau for real-time data monitoring and decision support<br>
Deployed machine learning models on cloud platforms (Azure, AWS) to enhance operational efficiency<br>
Implemented reinforcement learning techniques for optimizing supply chain logistics<br>
Utilized NLP tools like SpaCy and NLTK for text analysis and sentiment classification<br>
Conducted A/B testing and statistical analysis to validate model performance<br>
Managed big data technologies such as Apache Hadoop and Spark for large-scale data processing<br>
Automated data workflows using Docker and Kubernetes for scalable solutions</p>

Machine Learning Engineer

Intagleo
May 2015 - Jun 2018 · 3 years 1 month

<p>Developed and maintained data pipelines for processing transactional data using SQL and Python<br>
Implemented machine learning models for fraud detection and prevention, increasing detection accuracy by 30%<br>
Conducted feature engineering and selection to enhance model performance<br>
Developed data visualization tools using Matplotlib and Seaborn for business intelligence reporting<br>
Applied ensemble methods (Random Forest, Gradient Boosting) for classification tasks<br>
Built recommendation systems for personalized user experiences, leveraging collaborative filtering techniques<br>
Integrated machine learning solutions with existing systems using APIs and microservices<br>
Collaborated with cross-functional teams to align data science initiatives with business objectives<br>
Utilized Git and Jenkins for version control and continuous integration in model deployment<br>
Conducted regular model evaluation, selection, and validation to ensure robustness and reliability</p>

Skills

C++ Language (C++) MATLAB Python SQL Database Data Science Django Machine Learning Algorithms MATLAB and Mathematica Model Validation Pytorch Machine Learning SQL Java Scala MATLAB Hypothesis Testing A/B Testing Statistical Methods Supervised Learning Unsupervised Learning Reinforcement Learning Deep Learning Neural Networks Convolutional Neural Networks Recurrent Neural Networks Long Short-Term Memory Gradient Boosting Decision Trees Random Forest Support Vector Machines K-Nearest Neighbors K-Means TensorFlow Keras PyTorch scikit-learn XGBoost LightGBM CatBoost Data Cleaning Data Visualization Data Wrangling Feature Engineering Feature Selection Model Evaluation Model Selection Cross-Validation Hyperparameter Tuning Regularization NLTK spaCy Gensim Transformers Generative Adversarial Networks Autoencoders Q-learning Deep Q Networks Policy Gradient Methods
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