Data Scientist
4 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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