Jafar Alam

Data Scientist

Hyderabad, India 3+ yrs exp 88 · Excellent

About

Data Scientist with 3 years of experience in developing data-driven and AI/ML solutions using Python, SQL, Scikit-learn, Pandas, NumPy, TensorFlow, and PyTorch. Experienced in collecting, cleaning, preprocessing, and analyzing structured and unstructured datasets and developing supervised and unsupervised machine learning models for predictive analytics. Hands-on experience with NLP, Generative AI, LLMs, RAG, embeddings, and vector databases. Skilled in integrating ML models into applications using REST APIs and FastAPI, with working knowledge of Docker, Git, cloud platforms, and MLOps practices. Strong ability to collaborate with cross-functional teams to convert business requirements into scalable AI-powered solutions.

Skills & Expertise (78)

Python Advanced

8.4/10

Years Exp

Pandas Advanced

8.2/10

Years Exp

NumPy Advanced

8.0/10

Years Exp

SQL Advanced

7.5/10

Years Exp

AWS Intermediate

7.5/10

Years Exp

TensorFlow Intermediate

7.5/10

Years Exp

Git Advanced

7.5/10

Years Exp

Docker Intermediate

7.0/10

Years Exp

RAG Concepts MSE Prompt Engineering LLM Fundamentals Embeddings TF-IDF Tokenization Text Preprocessing Confusion Matrix ROC-AUC F1-Score Recall Precision Accuracy R² RMSE R² FastAPI REST APIs API Integration Azure GCP MLflow Model Monitoring GitHub Cd fundamentals Power BI Excel scikit-learn PyTorch MySql Postgresql VS Code Regression Analysis Feature Engineering Ensemble Learning Clustering Classification Regression Unsupervised Learning Supervised Learning Confidence Intervals A/B testing Hyperparameter Tuning Correlation Hypothesis Testing Probability Descriptive Statistics Google Colab Jupyter Notebook Seaborn Matplotlib SVM Demand Forecasting Prophet SARIMA ARIMA ACF Time Series Analysis PCA K-Means MAE KNN XGBoost Gradient Boosting Random Forest Decision Tree Logistic Regression Linear Regression Cross-Validation

Work Experience

Associate Data Scientist

AR SOFT CON Pvt. Ltd.

Jan 2023 - Present

Designed, developed, trained, and evaluated machine learning and deep learning models for business and predictive analytics use cases. Collected and integrated structured and unstructured datasets from multiple sources for analytical and machine learning applications. Conducted statistical analysis and supported experimentation, including hypothesis testing and A/B testing concepts, to evaluate business changes and decisions. Performed data cleaning, preprocessing, exploratory data analysis, feature engineering, and transformation using Python, Pandas, and NumPy. Developed supervised learning models for classification and regression problems and unsupervised learning models for clustering and pattern identification. Applied algorithms including Logistic Regression, Linear Regression, Decision Trees, Random Forest, Gradient Boosting, XGBoost, SVM, KNN, and K-Means. Used cross-validation and hyperparameter tuning to optimize model performance and improve generalization. Evaluated models using appropriate metrics such as Accuracy, Precision, Recall, F1-score, ROC-AUC, MAE, MSE, RMSE, and R2. Developed NLP solutions involving text preprocessing, tokenization, TF-IDF, Word2Vec, text classification, and semantic similarity. Worked with Generative AI and LLM-based applications, including prompt engineering, embeddings, RAG pipelines, and vector databases. Integrated trained ML models into applications through REST APIs and FastAPI. Containerized ML applications using Docker and maintained source code using Git/GitHub. Used cloud services such as AWS/Azure/GCP for experimentation, deployment, or ML application development. Implemented basic MLOps practices including experiment tracking, model versioning, deployment, and monitoring. Monitored deployed model performance and analyzed prediction quality to identify opportunities for model improvement. Collaborated with Data Engineers, Software Engineers, business stakeholders, and cross-functional teams to understand requirements and deliver AI-powered solutions. Participated in technical discussions, POCs, model optimization, testing, documentation, and production deployment activities. Continuously explored emerging developments in Machine Learning, Deep Learning, Generative AI, and LLM technologies.