- Collect, clean, and preprocess data from various sources.
- Perform exploratory data analysis to understand patterns, trends, and relationships.
- Identify data quality issues and implement solutions for data integrity.
- Develop predictive models using statistical techniques and machine learning algorithms.
- Evaluate model performance, tune hyperparameters, and optimize algorithms.
- Implement and deploy models into production systems for real-time decision-making.
- Collaborate with stakeholders to define business problems and objectives.
- Translate data insights into actionable recommendations and strategic initiatives.
- Create visualizations and dashboards to communicate findings effectively.
- Conduct A/B testing and experimentation to measure the impact of data-driven solutions.
- Explore new technologies, tools, and methodologies to improve analytical capabilities.
- Drive innovation by proposing and implementing data science projects that add value.
- Work closely with data engineers, software developers, and business analysts to integrate data solutions.
- Communicate findings and insights to non-technical stakeholders in a clear and understandable manner.
- Collaborate with teams across the organization to drive data-driven decision-making and business growth.
- Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a related field.
- Proven experience in data analysis, statistical modeling, and machine learning techniques.
- Proficiency in programming languages such as Python, R, or Java.
- Experience with data manipulation tools (e.g., SQL, Pandas, NumPy) and machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch).
- Strong analytical and problem-solving skills with the ability to work with large and complex datasets.
- Excellent communication skills and the ability to collaborate effectively in a team environment.
- Experience with cloud platforms (e.g., IBM Cloud, AWS, Azure) and big data technologies (e.g., Hadoop, Spark) is a plus.