Data Science Engineer

We are seeking a highly skilled Machine Learning and Data Science Engineer with expertise in Python and a deep understanding of modern machine learning algorithms. The ideal candidate will have a strong background in statistical modeling, machine learning, and scalable system design. You will play a pivotal role in creating and deploying data-driven solutions that optimize decision-making and drive business innovation.

Key Responsibilities

1. Data Analysis and Preprocessing

• Collect, clean, and preprocess structured and unstructured data from multiple sources.
• Conduct exploratory data analysis (EDA) to identify trends and patterns.
• Ensure data integrity, accuracy, and consistency.

2. Algorithm Development and Optimization

• Design and implement machine learning algorithms, including linear models, decision trees, and ensemble methods (e.g., Random Forest, AdaBoost, XGBoost, LightGBM).
• Optimize hyperparameters using techniques such as grid search, random search, and Bayesian optimization.
• Conduct feature engineering to improve model performance.

3. Model Deployment and Monitoring

• Deploy machine learning models into production using frameworks like Flask, FastAPI, or Docker.
• Monitor and evaluate model performance, retraining as necessary to ensure reliability over time.
• Implement pipelines for automated model training and deployment (MLOps).

4. Advanced Techniques

• Apply advanced algorithms for classification, regression, and clustering tasks.
• Work with boosting and bagging techniques to improve model accuracy and robustness.
• Explore time-series forecasting, NLP, or computer vision, depending on project needs.

5. Collaboration and Communication

• Partner with data engineers, software developers, and product teams to deliver end-to-end solutions.
• Translate complex findings into actionable insights for technical and non-technical stakeholders.

6. Research and Innovation

• Stay updated on the latest advancements in machine learning and data science.
• Experiment with and implement cutting-edge tools and methodologies to enhance workflows.

7. Python:

• Ability to write robust code in Python or R
• Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn).
• Understanding of algorithms like K[1]NN, Naive Bayes , SVM , Decision Forests etc
• Experience with data visualization tools like D3.js , Ggplot etc is desired .
• Knowledge of databases like MYSQL , NOSQL is desired .