Data Scientist

Key Responsibilities

• Collect, clean, transform, and analyze structured and unstructured data.
• Develop statistical and machine learning models for prediction and classification.
• Identify patterns, trends, and business opportunities from large datasets.
• Perform exploratory data analysis (EDA) and feature engineering.
• Build, evaluate, and optimize machine learning models.
• Work with business and technical teams to understand data requirements.
• Create dashboards, reports, and visualizations to communicate insights.
• Develop and maintain data pipelines and analytical workflows.
• Validate model performance using appropriate evaluation metrics.
• Deploy and monitor machine learning models in production environments.
• Document methodologies, analyses, models, and results.
• Stay updated with emerging technologies in AI, machine learning, and data science.

Required Skills

• Strong programming skills in Python or R.
• Good knowledge of Statistics and Probability.
• Experience with Machine Learning algorithms and concepts.
• Proficiency in libraries such as Pandas, NumPy, Scikit-learn, and Matplotlib/Seaborn.
• Strong SQL skills and experience working with databases.
• Knowledge of data visualization tools such as Power BI, Tableau, or similar.
• Understanding of supervised and unsupervised learning.
• Familiarity with model evaluation, feature engineering, and hyperparameter tuning.
• Knowledge of Git/GitHub and version control.
• Strong analytical and problem-solving abilities.

Preferred Skills

• Experience with Deep Learning frameworks such as TensorFlow or PyTorch.
• Knowledge of NLP, Generative AI, or Computer Vision.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud.
• Familiarity with Docker and ML deployment/MLOps practices.
• Experience working with big-data technologies such as Spark is a plus.