Intermediate Data Scientist
2 weeks ago
Job Description
Job Title: Intermediate Data Scientist
Job Overview:
As an Intermediate Data Scientist, you will leverage your data analysis and machine learning skills to derive actionable insights and support data-driven decision-making. You will work with large datasets, apply statistical methods, and build machine learning models to solve complex business problems.Responsibilities:
Data Analysis: Analyze and explore large datasets to identify trends, patterns, and anomalies. Use statistical techniques and data visualization tools to present findings effectively. Machine Learning: Build, train, and evaluate machine learning models for various applications, such as predictive modeling, recommendation systems, and classification. Data Preprocessing: Prepare and clean data for analysis, addressing issues such as missing values, outliers, and data normalization. Feature Engineering: Create relevant features and attributes to improve the performance of machine learning models. Model Evaluation: Assess the performance of models using appropriate evaluation metrics and techniques. Fine-tune models to optimize performance. Data Integration: Collaborate with cross-functional teams to integrate data science solutions into applications, systems, and processes. Data Visualization: Create data visualizations and dashboards to communicate insights effectively to non-technical stakeholders. Experimentation: Design and conduct experiments to test hypotheses and evaluate the impact of data-driven decisions. Documentation: Maintain clear documentation of methodologies, findings, and code to ensure transparency and reproducibility. Continual Learning: Stay updated on the latest data science techniques, tools, and technologies. Share knowledge and best practices with the team.Qualifications:
Bachelor's or higher degree in a quantitative field, such as Computer Science, Statistics, Mathematics, or Data Science. 2-5 years of relevant work experience in data analysis and machine learning. Proficiency in programming languages such as Python or R for data analysis and machine learning. Solid understanding of statistical analysis and hypothesis testing. Experience with data manipulation libraries (e.g., Pandas, NumPy) and machine learning frameworks (e.g., scikit-learn, TensorFlow, or PyTorch). Familiarity with data visualization tools like Matplotlib, Seaborn, or Tableau. Knowledge of SQL for data extraction and manipulation. Strong problem-solving skills and the ability to work with large datasets. Excellent communication and collaboration skills to work with non-technical stakeholders. Experience with version control (e.g., Git) and agile methodologies is a plus. Certifications in data science or machine learning are advantageous.-
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