
Data Scientist
3 days ago
Responsibilities:
• Utilize Python programming language to clean, transform, and manipulate large datasets for analysis.
• Develop and implement statistical models, machine learning algorithms, and predictive analytics to solve complex business problems.
• Apply advanced data mining techniques and statistical analyses to identify patterns, correlations, and trends in data.
• Design, develop, and deploy machine learning models and algorithms.
• Perform exploratory data analysis, data visualization, and statistical analysis to uncover insights, patterns and trends in the data.
• Optimize and tune machine learning models for better performance.
• Evaluate and select appropriate tools and technologies for data science projects.
• Continuously monitor and evaluate the performance of models and algorithms and make necessary adjustments to ensure they remain accurate and effective.
• Design and execute experiments to test hypotheses and validate models.
• Collaborate with cross-functional teams, including engineers, analysts, and business stakeholders, to understand project requirements and deliver actionable insights.
• Communicate findings and insights effectively to both technical and non-technical audiences through visualizations, reports, and presentations.
• Stay up to date with the latest advancements in machine learning and data science and propose innovative approaches to solving business challenges.
Qualifications:
• Should have experience in Data Management
• Knowledge of machine learning and Deep learning algorithms and data visualization tools
• Proficiency in python programming language.
• Familiarity with cloud technologies such as AWS, Azure, or Google Cloud.
• Strong problem-solving and analytical skills.
• Knowledge of data visualization tools
• Should have experience in Kafka, Redis, WebSocket
Requirements:
• Bachelor’s or master’s degree in computer science, Statistics, Mathematics, or a related field.
• Proven experience working as a Data Scientist, utilizing Python and machine learning techniques.
• Strong programming skills in Python, including libraries such as Pandas, NumPy, and Scikit-learn.
• Solid understanding of machine learning algorithms, such as regression, classification, clustering, and deep learning.
• Proficiency in data visualization tools like Matplotlib, Seaborn, or Tableau.
• Experience with SQL and relational databases for data extraction and manipulation.
• Familiarity with big data technologies and frameworks (e.g., Hadoop, Spark) is a plus.
• Strong analytical and problem-solving skills, with a keen attention to detail.
• Excellent communication and collaboration skills, with the ability to explain complex concepts to both technical and non-technical stakeholders.
• A self-driven and proactive mindset, with the ability to work independently and manage multiple projects simultaneously.
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