Responsibilities:
- Collaborate with business stakeholders to understand their data needs and objectives.
- Collect, clean, and preprocess data from various sources for analysis.
- Perform exploratory data analysis to identify trends, patterns, and correlations.
- Develop and implement predictive models and machine learning algorithms to solve business challenges.
- Apply statistical analysis techniques to analyze complex datasets and draw meaningful conclusions.
- Create data visualizations and reports to communicate insights effectively to non-technical audiences.
- Collaborate with data engineers to optimize data pipelines for efficient data processing.
- Conduct A/B testing and experimentation to evaluate the effectiveness of different strategies.
- Stay up-to-date with advancements in data science, machine learning, and artificial intelligence.
- Assist in the development and deployment of machine learning models into production environments.
- Provide data-driven insights and recommendations to support strategic decision-making.
- Collaborate with other data scientists, analysts, and cross-functional teams to drive data initiatives.
Requirements:
- Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, or a related field (or equivalent practical experience).
- Proven experience as a Data Scientist or similar role, with a portfolio of data science projects that demonstrate your analytical skills.
- Proficiency in programming languages such as Python or R for data manipulation and analysis.
- Strong understanding of statistical analysis, machine learning algorithms, and data visualization techniques.
- Experience with machine learning frameworks and libraries (e.g., scikit-learn, TensorFlow, PyTorch).
- Familiarity with data manipulation libraries (e.g., Pandas, NumPy) and data visualization tools (e.g., Matplotlib, Seaborn).
- Solid understanding of SQL and database concepts for querying and extracting data.
- Excellent problem-solving skills and the ability to work with complex, unstructured datasets.
- Effective communication skills to explain technical concepts to non-technical stakeholders.
- Experience with big data technologies (e.g., Hadoop, Spark) is a plus.
- Knowledge of cloud platforms and services for data analysis (e.g., AWS, Azure) is advantageous.
- Familiarity with natural language processing (NLP) and text analysis is a plus.
- Advanced degree (Master's or PhD) in a related field is beneficial but not required.
