Key Responsibilities:
Data Science & Statistical Modeling
- Apply statistical techniques including hypothesis testing (T-Test, Z-Test) for data-driven decision-making.
- Develop regression models (linear and logistic) for predictive analytics.
- Build classification models using Decision Trees, SVM, and other ML algorithms.
- Perform probabilistic modeling and graph-based analytical methods.
Machine Learning & Model Development
- Design and implement ML solutions using Python, PySpark, and R.
- Work with ML frameworks such as TensorFlow, PyTorch, Scikit-learn, Keras, CNTK, and MXNet.
- Implement forecasting models including ARIMA, ARIMAX, and exponential smoothing.
- Apply distance metrics (Euclidean, Manhattan, Hamming) for model development and clustering tasks.
Data Quality & Model Monitoring
- Use tools such as Great Expectations for data validation and quality checks.
- Implement model monitoring and performance tracking using Evidently AI.
- Ensure model reliability, accuracy, and continuous improvement in production environments.
MLOps & Deployment
- Deploy machine learning models using tools such as Kubeflow and BentoML.
- Support end-to-end ML lifecycle including training, validation, deployment, and monitoring.
- Collaborate with engineering teams to integrate ML models into production systems.
Data Analysis & Programming
- Perform advanced data analysis using Python, PySpark, SAS, SPSS, and R.
- Work with large-scale datasets and perform feature engineering and data preprocessing.
- Build reproducible and scalable analytical workflows.
Leadership & Project Management
- Lead and manage data science projects from ideation to deployment.
- Guide and mentor data science teams and ensure technical excellence.
- Coordinate with cross-functional teams to define requirements and deliver solutions.
- Ensure alignment of data science initiatives with business goals and KPIs.
Collaboration & Stakeholder Management
- Communicate analytical findings and insights to business and executive stakeholders.
- Translate complex statistical outputs into actionable business recommendations.
- Work closely with engineering, product, and business teams.
