Description

Experience : 5-8 years Location: Hyderabad(Uppal) Notice Period:Immediate-15days Job Description: Role: We are seeking an enthusiastic and high energy-driven Data Science Engineer to join our team. The ideal candidate should have proven experience in Data Science and Data Analysis domains. Key Responsibilities: Develop, implement, and maintain data models using machine learning and deep learning algorithms. • Collaborate with cross-functional teams to gather and analyze data. • Work on cloud platforms and utilize tools like Docker, Cloud Functions, and Big Query. Utilize visualization tools to represent data in a comprehensible format. Data Modeling and Algorithm Development: Design, develop, and deploy machine learning and deep learning models to solve business challenges. • Continuously refine models based on new data and feedback to improve performance. • Collaborate with domain experts to understand the business context and fine-tune the models accordingly. Collaborate with domain experts to refactor existing code as needed to improve. Data Analysis and Insights Generation: Analyze large datasets to uncover patterns, trends, and actionable insights. Create detailed reports and visualizations for stakeholders to provide a clear understanding of data-driven findings Collaboration: • Work closely with cross-functional teams, including data engineers, software developers, and business analysts, to integrate data science models into business processes and applications. • Participate in team meetings to understand requirements, provide insights, and offer recommendations based on data analysis. Cloud and Infrastructure Management: Leverage cloud platforms, such as GCP, to deploy, monitor, and scale data science applications. • Use tools like Docker for containerization and ensure seamless deployment of models and applications across different environments Data Visualization: Utilize various visualization tools and libraries like Matplotlib, Seaborn, and Plotly to create intuitive dashboards and reports. • Collaborate with stakeholders to customize visualizations based on their needs and preferences. Continuous Learning and Research: Stay updated with the latest advancements in the field of data science, machine learning, and artificial intelligence. Explore and experiment with new algorithms, tools, and technologies to enhance the efficiency and effectiveness of data-driven solutions Quality Assurance and Testing: Ensure the robustness and accuracy of models by conducting thorough testing and validation. Work with the quality assurance team to address potential issues and refine models based on feedback. Documentation: • Document processes, methodologies, and results to ensure transparency and reproducibility. • Create user manuals and guides for developed tools and models to facilitate their use by non- technical stakeholders. Required Skills: • Proficiency in languages: Python, SQL Familiarity with Libraries and Frameworks: MATLAB, Scikit-Learn, NumPy, Pandas, SciPy, Keras, TensorFlow, PyTorch, Django, Flask, and Fast-API. • Experience with Visualization Tools: Matplotlib, Seaborn, Plotly, Streamlit. Knowledge of Databases: SQL, MongoDB, Redis, Etc. • Comfortable with IDEs: Jupyter Notebook, VS Code, Spyder. Preferred Experience: • 5 to 8 years previous experience as a Full Stack Engineer, Data Science Engineer, or a similar role. • Familiarity with NLP and Computer Vision. • Experience with GCP services is a plus. Education: Degree, e.g., bachelor's/masters in computer science or equivalent.

Role and Responsibilities

• Experience : 5-8 years Location: Hyderabad(Uppal) Notice Period:Immediate-15days Job Description: Role: We are seeking an enthusiastic and high energy-driven Data Science Engineer to join our team. The ideal candidate should have proven experience in Data Science and Data Analysis domains. Key Responsibilities: Develop, implement, and maintain data models using machine learning and deep learning algorithms. • Collaborate with cross-functional teams to gather and analyze data. • Work on cloud platforms and utilize tools like Docker, Cloud Functions, and Big Query. Utilize visualization tools to represent data in a comprehensible format. Data Modeling and Algorithm Development: Design, develop, and deploy machine learning and deep learning models to solve business challenges. • Continuously refine models based on new data and feedback to improve performance. • Collaborate with domain experts to understand the business context and fine-tune the models accordingly. Collaborate with domain experts to refactor existing code as needed to improve. Data Analysis and Insights Generation: Analyze large datasets to uncover patterns, trends, and actionable insights. Create detailed reports and visualizations for stakeholders to provide a clear understanding of data-driven findings Collaboration: • Work closely with cross-functional teams, including data engineers, software developers, and business analysts, to integrate data science models into business processes and applications. • Participate in team meetings to understand requirements, provide insights, and offer recommendations based on data analysis. Cloud and Infrastructure Management: Leverage cloud platforms, such as GCP, to deploy, monitor, and scale data science applications. • Use tools like Docker for containerization and ensure seamless deployment of models and applications across different environments Data Visualization: Utilize various visualization tools and libraries like Matplotlib, Seaborn, and Plotly to create intuitive dashboards and reports. • Collaborate with stakeholders to customize visualizations based on their needs and preferences. Continuous Learning and Research: Stay updated with the latest advancements in the field of data science, machine learning, and artificial intelligence. Explore and experiment with new algorithms, tools, and technologies to enhance the efficiency and effectiveness of data-driven solutions Quality Assurance and Testing: Ensure the robustness and accuracy of models by conducting thorough testing and validation. Work with the quality assurance team to address potential issues and refine models based on feedback. Documentation: • Document processes, methodologies, and results to ensure transparency and reproducibility. • Create user manuals and guides for developed tools and models to facilitate their use by non- technical stakeholders. Required Skills: • Proficiency in languages: Python, SQL Familiarity with Libraries and Frameworks: MATLAB, Scikit-Learn, NumPy, Pandas, SciPy, Keras, TensorFlow, PyTorch, Django, Flask, and Fast-API. • Experience with Visualization Tools: Matplotlib, Seaborn, Plotly, Streamlit. Knowledge of Databases: SQL, MongoDB, Redis, Etc. • Comfortable with IDEs: Jupyter Notebook, VS Code, Spyder. Preferred Experience: • 5 to 8 years previous experience as a Full Stack Engineer, Data Science Engineer, or a similar role. • Familiarity with NLP and Computer Vision. • Experience with GCP services is a plus. Education: Degree, e.g., bachelor's/masters in computer science or equivalent.