Data Scientist

Key Responsibilities

• Develop and implement machine learning models for classification, regression, forecasting, clustering, and predictive analytics.
• Apply statistical and machine learning techniques to identify patterns, trends, and business opportunities from large and complex datasets.
• Build and optimize models using algorithms such as XGBoost, Random Forest, Gradient Boosting, Logistic/Linear Regression, Decision Trees, and clustering techniques.
• Perform data preprocessing, feature engineering, feature selection, and exploratory data analysis (EDA).
• Evaluate model performance using appropriate metrics and techniques such as cross-validation, hyperparameter tuning, ROC-AUC, Precision, Recall, F1-score, RMSE, and MAE.
• Translate business requirements into analytical and machine learning solutions.
• Identify relevant data sources, assess data quality, and work with structured and unstructured datasets.
• Conduct statistical analysis and hypothesis testing to support data-driven decision-making.
• Interpret model outputs and communicate insights, assumptions, and recommendations to both technical and non-technical stakeholders.
• Collaborate with Data Engineers, Software Engineers, Product Managers, and business teams to productionize analytical solutions.
• Monitor model performance and continuously improve models based on changing data and business requirements.
• Stay current with emerging developments in machine learning, statistics, and AI.

Required Skills & Experience

• Strong hands-on experience in Machine Learning and Statistical Modelling.
• Strong understanding of supervised and unsupervised learning techniques.
• Experience with:XGBoost
  Random Forest
  Linear & Logistic Regression
  Decision Trees
  Classification & Regression
  Clustering algorithms such as K-Means and hierarchical clustering

Must have skills: 

Machine Learning

Statistical Modelling(XGBoost, Random Forest, regression, classification, clustering,)

Good to have Gen AI