Data Scientist

Key Responsibilities

• Collect, clean, process, and analyze large datasets.
• Develop and implement Machine Learning models.
• Perform exploratory data analysis (EDA) to identify trends and patterns.
• Build predictive and statistical models to solve business problems.
• Apply supervised and unsupervised learning techniques.
• Develop data-driven solutions using Python and SQL.
• Perform feature engineering and model optimization.
• Evaluate model performance using appropriate metrics.
• Create dashboards and reports to communicate insights.
• Collaborate with business analysts, engineers, and stakeholders.
• Deploy and monitor Machine Learning models.
• Document methodologies, findings, and project outcomes.

Preferred Skills

• Strong programming knowledge in Python or R.
• Hands-on experience with SQL, MySQL, or PostgreSQL.
• Knowledge of Python libraries such as Pandas, NumPy, Matplotlib, and Seaborn.
• Experience with Machine Learning algorithms such as Linear Regression, Logistic Regression, Decision Trees, Random Forest, SVM, KNN, and XGBoost.
• Strong understanding of Statistics, Probability, Hypothesis Testing, and Predictive Modeling.
• Experience in Exploratory Data Analysis (EDA), Data Cleaning, and Feature Engineering.
• Knowledge of Deep Learning frameworks such as TensorFlow, PyTorch, or Keras.
• Experience with data visualization tools such as Power BI, Tableau, or Looker.
• Knowledge of NLP, Generative AI, and Large Language Models (LLMs).
• Familiarity with cloud platforms such as AWS, Azure, or Google Cloud.
• Knowledge of MLOps, Docker, Git, and model deployment.
• Strong analytical, problem-solving, and communication skills.
• Ability to translate complex data findings into actionable business recommendations.