Data Scientist

Key Responsibilities:

• Collect, clean, and preprocess large volumes of structured and unstructured data from various sources to prepare for analysis.
• Perform exploratory data analysis to identify patterns, trends, and correlations in the data, and generate hypotheses for further investigation.
• Develop and implement machine learning models and algorithms to solve complex business problems, such as predictive modeling, classification, clustering, and recommendation systems.
• Evaluate and fine-tune model performance using techniques such as cross-validation, hyperparameter tuning, and ensemble methods.
• Interpret and communicate findings and insights from data analysis to stakeholders in a clear and actionable manner, using data visualization and storytelling techniques.
• Collaborate with business stakeholders to understand requirements, define success criteria, and prioritize projects based on business impact.
• Stay updated on the latest developments in data science, machine learning, and artificial intelligence, and apply new techniques and methodologies to improve model performance and accuracy.
• Work closely with data engineers, software developers, and other stakeholders to deploy and integrate machine learning models into production systems.

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Requirements:

• Master's or Ph.D. degree in Computer Science, Statistics, Mathematics, or a related field. Equivalent work experience may be considered.
• Proven experience in data science or related roles, with a strong foundation in statistical analysis, machine learning, and data mining techniques.
• Proficiency in programming languages such as Python, R, or SQL for data manipulation, analysis, and modeling.
• Experience with machine learning libraries and frameworks such as scikit-learn, TensorFlow, or PyTorch.
• Strong problem-solving skills and ability to think critically and creatively to solve complex business problems.
• Excellent communication and collaboration skills, with the ability to work effectively in a cross-functional team environment.
• Experience with big data technologies such as Hadoop, Spark, or Kafka is a plus.
• Strong understanding of software engineering principles and best practices for building scalable and maintainable machine learning systems.