Careerfit.ai
Location
Bangalore | India
Job description
Responsibilities: Develop end-to-end data science solutions, encompassing data collection, preprocessing, model development, deployment, and ongoing monitoring. Conduct exploratory data analysis (EDA) to gain insights into data characteristics, patterns, and relationships, informing subsequent modeling efforts. Design and implement machine learning models and algorithms to address business problems and derive actionable insights from data. Create and maintain data pipelines and workflows to automate data processing, feature engineering, and model training processes, ensuring efficiency and scalability. Build interactive data visualizations and intuitive dashboards to effectively communicate insights and findings to stakeholders at all levels of the organization. Collaborate closely with cross-functional teams to seamlessly integrate data science solutions into software applications and business processes, driving innovation and efficiency. Stay abreast of the latest developments in data science techniques, tools, and best practices, continually enhancing expertise and contributing to a culture of learning and innovation. Requirements: Bachelor's degree in Computer Science, Statistics, Mathematics, or a related field. Proven experience as a data scientist or machine learning engineer, with a demonstrated track record of delivering impactful data science solutions. Strong proficiency in programming languages commonly used in data science, including Python, R, and SQL. Experience with popular machine learning frameworks and libraries such as TensorFlow, scikit-learn, and PyTorch. Proficiency in data visualization tools and techniques, such as Matplotlib, Seaborn, and Plotly, to effectively communicate insights. Excellent problem-solving, analytical, and communication skills, with the ability to convey complex technical concepts to non-technical stakeholders. Skills: Data Science: Expertise in developing machine learning models and algorithms to solve business problems. Data Engineering: Proficiency in building data pipelines and workflows for data preprocessing and model training. Data Visualization: Ability to create interactive visualizations and dashboards to communicate insights effectively. Collaboration: Strong teamwork and collaboration skills to work effectively within cross-functional teams. Preferred: Advanced degree (Master's or Ph.D.) in a relevant field, showcasing advanced knowledge and expertise in data science. Certification in data science or machine learning, demonstrating a commitment to continuous learning and professional development. Proficiency in cloud computing platforms (e.g., AWS, Azure, Google Cloud Platform) for deploying and scaling data science solutions. Experience with containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes) for managing data science applications in containerized environments.
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