Data Scientist

Data analysis & business understanding

• Collaborate with business stakeholders to understand business challenges, define analytical objectives, and identify opportunities for advanced analytics and machine learning.
• Translate business requirements into analytical approaches and data science solutions.
• Identify relevant data sources and define data requirements for analytical projects.
• Assess the feasibility of analytical solutions based on available data and business objectives.
• Present analytical findings and recommendations to both technical and non-technical stakeholders.
• Document methodologies, assumptions, and analytical outcomes throughout each project.

Data exploration & preparation

• Explore, profile, and analyze structured and unstructured datasets to identify patterns, trends, and anomalies.
• Perform data cleaning, preprocessing, and transformation to prepare high-quality datasets for analysis and modeling.
• Engineer meaningful features to improve model performance and business relevance.
• Assess data quality and identify issues that may impact analytical results.
• Collaborate with Data Engineers to ensure scalable and reliable data pipelines.
• Maintain documentation of datasets, transformations, and feature engineering processes.

Machine learning & predictive modeling

• Develop, train, evaluate, and optimize machine learning models for classification, regression, clustering, forecasting, recommendation, and anomaly detection.
• Select appropriate algorithms and statistical techniques based on business requirements and data characteristics.
• Perform feature selection, hyperparameter tuning, and model optimization.
• Validate model performance using appropriate evaluation metrics and statistical methods.
• Interpret model results and communicate business impact clearly.
• Continuously improve models based on new data, business feedback, and changing requirements.

Data visualization & insights

• Develop clear visualizations and dashboards to communicate analytical findings.
• Present insights and recommendations supported by data and statistical evidence.
• Explain complex analytical concepts in a way that is accessible to business stakeholders.
• Support business teams in interpreting analytical outputs and integrating insights into decision-making.
• Produce clear technical documentation describing analytical methodologies and model performance.

Model deployment & continuous improvement

• Collaborate with Data Engineers and Software Engineers to deploy analytical models into production environments.
• Monitor model performance and identify opportunities for retraining or optimization.
• Support implementation of automated machine learning workflows where appropriate.
• Perform impact analysis and validate model performance after deployment.
• Contribute to best practices for model lifecycle management, documentation, and reproducibility.
• Continuously improve analytical processes, reusable assets, and team standards.

Testing & implementation support

• Collaborate with business analysts, testers, and development teams during testing and implementation.
• Validate analytical outputs and model predictions against business expectations.
• Investigate data issues and support defect analysis when needed.
• Ensure analytical solutions meet agreed business and technical requirements.
• Support production releases and monitor solution performance after implementation.