Data Scientist - Machine Learning & AI - Cloud Platforms

Key Responsibilities:

- Design, develop, and deploy machine learning and AI models for predictive analytics and business solutions.

- Perform data preprocessing, feature engineering, and exploratory data analysis using Python, Pandas, and related tools.

- Evaluate, fine-tune, and optimize model performance using best practices in ML/AI.

- Build and maintain end-to-end data pipelines for seamless integration of large datasets.

- Collaborate with cross-functional teams (engineering, product, and business) to translate requirements into data-driven solutions.

- Implement and manage ML workflows on cloud platforms (Azure, AWS, GCP), ensuring scalability and efficiency.

- Communicate insights and results effectively using visualization tools and dashboards.

- Stay updated on emerging AI/ML trends, cloud technologies, and best practices to continuously improve solutions.

Core Skills:

- Machine Learning & Artificial Intelligence (AI)

- Predictive Modeling & Statistical Analysis

- Data Preprocessing & Feature Engineering

- Model Evaluation & Optimization

- Problem-Solving & Business Acumen

Technical Skills:

- Programming: Python (expert), SQL (preferred)

- Libraries & Frameworks: Pandas, Scikit-learn, TensorFlow, Keras (optional: PyTorch)

- Cloud Platforms: Azure, AWS, GCP

- Data Visualization Tools: Matplotlib, Seaborn, Plotly, Tableau/Power BI

- Version Control & Collaboration: Git/GitHub

- Workflow & ML Lifecycle Tools: Airflow, MLflow (optional)

Education & Experience:

- Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.

- 5+ years of experience in data science, including building and deploying ML/AI models in production environments.

- Hands-on experience with cloud-based ML pipelines and large-scale data processing.

KPIs / Success Metrics:

- Accuracy, precision, recall, or other relevant performance metrics of deployed models.

- Time-to-deploy and reliability of ML pipelines.

- Business impact (e.g, revenue growth, cost reduction, operational efficiency).

- Number of actionable insights delivered to stakeholders.

- Model scalability, monitoring, and maintenance efficiency