Data Scientist

Programming & Tools Strong proficiency in Python (NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch) Working knowledge of statistical analysis and modeling Machine Learning & AI Solid understanding of machine learning algorithms (supervised & unsupervised) Hands-on experience with: Regression (linear, logistic), Classification (decision trees, random forests, SVM), Clustering (K-means, hierarchical) Predictive Modeling Proven experience in predictive modeling and forecasting Ability to build, validate, and deploy predictive models Strong understanding of: Feature engineering Model evaluation techniques (ROC, precision/recall, cross-validation) Experience working with real-world datasets to derive actionable insights Statistics & Data Analysis Strong foundation in statistics and probability Hypothesis testing, regression analysis, and statistical modeling Data cleaning, transformation, and exploratory data analysis (EDA) Data & Deployment (Preferred )Experience with cloud platforms (AWS, Azure, or GCP )Familiarity with Docker/containers is a plu sExposure to MLOps practices (CI/CD for ML models )

The Data Scientist will be responsible for programming and utilizing machine learning algorithms to derive actionable insights from real-world datasets. They will also build, validate, and deploy predictive models while ensuring proper statistical analysis and data cleaning.

Candidates should have strong proficiency in Python and a solid understanding of machine learning algorithms, along with hands-on experience in predictive modeling. A strong foundation in statistics and experience with cloud platforms is preferred.

Identified from the job description. Confirm important requirements above.

• Python
• NumPy
• Pandas
• Scikit-learn
• TensorFlow
• PyTorch
• Machine Learning
• AI
• Predictive Modeling
• Statistics
• Data Analysis
• Data Cleaning
• Feature Engineering
• Model Evaluation
• Cloud Platforms
• MLOps

Minimum experience

5+ years