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Today’s exceptional challenges require your unique skills. Together, we can build the future of data storage.
• Collect and extract large sets of structured and unstructured data from various sources such as databases, APIs, and web scraping.
• Develop predictive models using machine learning algorithms to forecast trends and future outcomes.
• Develop and integrate AI agents to automate data collection, preprocessing, and analysis tasks, enhancing efficiency and scalability.
• Utilize generative AI models to create synthetic data, augment data sets, and generate insights.
• Deploy models, systems, and AI agents developed; monitor and maintain them to ensure they are working effectively.
• Document processes and methodologies to ensure that work is reproducible and can be audited.
• Work with cross-functional teams such as product managers, software engineers, and business analysts to identify business problems and develop solutions.
• Explain complex technical concepts to non-technical stakeholders in a clear and concise manner.
• Master’s degree in a quantitative field such as Data Science, Computer Science, Statistics, Mathematics, Physics, or Engineering.
• Knowledge of the AI/ML lifecycle management and tools, including EDA, Modeling, Integration/Deployment, Data/Model drift detection, Model retraining, etc.
• Familiarity with core techniques in statistical and machine learning, e.g., regularized regression, time series analysis, decision trees, boosting algorithms, neural networks, clustering, and collaborative filtering.
• Experience working with structured, semi-structured, and unstructured data sources.
• Knowledge in web crawling, natural language processing, and visualization.
• Familiar with collaborative solutions, model & code versioning (Github), solution packaging (Docker), model deployment (Dataiku).
• Proficient in programming languages such as Python, R, and SQL (for data manipulation, cleaning, and analysis), Tableau, PowerBI or Spotfire, NumPy, Pandas, and Matplotlib for visualization.
• Experience in developing and deploying generative AI models and large language models (LLMs).
• Skills in Robotic Process Automation (RPA) and automation software development.
• Knowledge of UI and UX design principles to enhance user interaction and experience in data-driven applications.
• Ability to define problems, generate hypotheses, develop and test solutions, and effectively communicate complex technical concepts to non-technical stakeholders.
• Attention to detail.
• Ability to collaborate effectively with others to achieve project goals.
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