Data Scientist

The Data Scientist is responsible for analyzing, modeling, and interpreting complex datasets to generate actionable insights and support data-driven decision‐making. The role focuses on developing statistical models, machine learning algorithms, and analytical solutions that enhance organizational performance, optimize products and services, and enable strategic planning.

• Collect, clean, and preprocess structured and unstructured data from multiple sources.
• Analyze large datasets to identify trends, patterns, and insights that support business and strategic objectives.
• Design, develop, and validate statistical models and machine learning algorithms.
• Build predictive and prescriptive analytics solutions to support decision‑making.
• Collaborate with cross‑functional teams to translate business requirements into analytical solutions.
• Develop data visualizations, dashboards, and reports to communicate findings to technical and non‑technical stakeholders.
• Evaluate model performance and continuously improve accuracy, robustness, and scalability.
• Ensure data quality, integrity, and consistency across analytical outputs.
• Apply best practices in data governance, privacy, and ethical use of data.
• Document methodologies, models, and analytical processes.

• Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field.
• Master’s degree in Data Science, Artificial Intelligence, Statistics, or a related discipline is preferred.
• Minimum of 2–5 years of experience in data analysis, data science, or applied machine learning.
• Strong proficiency in programming languages such as Python or R.
• Experience with data manipulation and analysis libraries (e.g., Pandas, Num Py, Sci Py).
• Hands‑on experience with machine learning frameworks (e.g., Scikit‑learn, Tensor Flow, PyTorch).
• Solid understanding of statistical analysis, probability, and hypothesis testing.
• Experience with SQL and working with relational and non‑relational databases.
• Ability to communicate complex analytical concepts clearly to non‑technical audiences.

• Data Analysis and Statistical Modeling
• Machine Learning and Predictive Analytics
• Problem‑Solving and Critical Thinking
• Data Visualization and Storytelling
• Stakeholder Collaboration
• Attention to Detail and Data Quality
• Time Management and Prioritization
• Ethical Data Usage and Privacy Awareness

• Actionable insights that support strategic and operational decision‑making.
• Reliable and scalable data models that improve efficiency and performance.
• Improved data‑driven culture across the organization.
• Measurable impact through analytics and predictive insights.