Lead Data Scientist - Machine Learning Models

Responsibilities : - Develop and implement machine learning models, including regression, decision trees, and advanced predictive models to address complex business challenges and deliver actionable insights. - Build customer segmentation models to identify patterns and groupings in customer data, enabling targeted marketing, personalized services, and improved customer experience. - Develop and deploy time series forecasting models (e. g., ARIMA, Prophet) to predict trends, demand, and key business metrics to support decision-making and business strategy. - Clean, preprocess, and transform raw data into structured datasets. Identify key features that enhance model performance and interpretability. - Apply rigorous evaluation techniques such as cross-validation, hyperparameter tuning, and model diagnostics to optimize the performance of models. - Work closely with cross-functional teams (marketing, product, engineering, etc. ) to understand business needs and translate them into data science solutions. - Communicate findings, model insights, and business recommendations clearly to both technical and non-technical stakeholders. - Design and create compelling visualizations and dashboards to communicate model outputs and key metrics to drive data-driven : - Bachelor's or master's degree in data science, Computer Science, Statistics, Mathematics, or Engineering. - Proven experience in building and deploying machine learning models, particularly regression models, decision trees (e. g., Random Forest, Gradient Boosting), and predictive analytics. - Experience in customer segmentation and clustering techniques (e. g., K-means, Hierarchical clustering, DBSCAN). - Hands-on experience in time series forecasting models and techniques (e. g., ARIMA, Prophet, Holt-Winters). - Solid understanding of model evaluation techniques (e. g., RMSE, MAE, R2 confusion matrices) and performance optimization, hyperparameter tuning approaches (e. g., GridSearchCV, RandomizedSearchCV). - Proficiency in Python (Pandas, NumPy, Scikit-learn, XGBoost, TensorFlow, etc). - Strong SQL skills for data extraction and manipulation. - Experience with data visualization libraries (e. g., Matplotlib, Seaborn, Plotly) and/or BI tools (e. g., Power BI, Tableau). - Experience with AWS cloud platforms for model deployment and scaling. - Knowledge of advanced machine learning techniques like ensemble methods (e. g., Bagging, Boosting), neural networks, and anomaly detection. - Familiarity with version control (e. g., Git), Agile methodologies, and CI/CD practices in a data science context. - Understanding of A/B testing and other experimental methodologies to validate models and business strategies. (ref:hirist.tech)

Location: bangalore, IN

Posted Date: 5/1/2025