Data Scientist

Role:

Experience:

Open Positions:

Key Skills:

• Python, SQL, Machine Learning (e.g., scikit-learn, XGBoost)
• Data Analysis, Statistical Modeling, Data Visualization
• Data Pipeline Tools (e.g., Azure Data Factory, Synapse, Databricks)
• BI Tools (e.g., Power BI, Tableau) or Python libraries (e.g., Plotly, Matplotlib, Seaborn)

Responsibilities:

• Build and deploy machine learning models (e.g., classification, regression, clustering, forecasting) using frameworks like scikit-learn and cloud-based ML services to address business needs.
• Analyze structured and unstructured data using Python, SQL, and relevant analytics tools to uncover trends and generate actionable insights.
• Design, implement, and automate data pipelines using tools like Azure Data Factory, Synapse, Databricks, or similar for preprocessing, feature engineering, and model deployment.
• Create interactive dashboards and reports using BI tools or Python visualization libraries to present data and insights to stakeholders effectively.
• Collaborate with cross-functional teams to define analytical objectives, translate business problems into data science solutions, and communicate results clearly.
• Evaluate and validate model performance using techniques such as cross-validation, A/B testing, and drift detection.
• Integrate models into production systems and applications via APIs or decision support tools, ensuring scalability and maintainability.
• Follow data governance, security, and privacy standards including access control, encryption, and responsible AI compliance.
• Maintain detailed documentation of data pipelines, models, assumptions, and analytical workflows for transparency and reproducibility.
• Stay informed on advancements in machine learning and data engineering tools to incorporate best practices and foster innovation.