Director, AI & ML

at  Confluent

Position at Confluent Inc
With Confluent, organizations can harness the full power of continuously flowing data to innovate and win in the modern digital world. We have a purpose that drives us to do better every day – we’re creating an entirely new category within data infrastructure - data streaming. This technology will allow every organization to create experiences and use the power of data in ways that profoundly impact the way we all live. This impact is our purpose and drives us to do better every day.
One Confluent. One team. One Data Streaming Platform.
Data Connects Us.

ABOUT THE ROLE:

Do you love pushing the boundaries of innovation and driving business initiatives through cutting-edge AI and machine learning work? Join us in shaping the future!
The AI/ML Data Science Team is at the heart of our company, building the central nervous system that drives better and faster decisions through data and predictive models. We are seeking an inspirational leader to guide a team of highly skilled data scientists in creating advanced forecasting models, predictive solutions, and state-of-the-art GenAI applications.
We need a hands-on independent leader with expertise in various time series techniques and supervised and unsupervised modeling expertise. This role also requires a strong advocate for MLOps to ensure team productivity and efficiency, along with a deep understanding of open-source libraries like MLFlow and Hydra to build scalable models with best practices.
If you are a visionary with a passion for AI/ML and a knack for fostering innovation, we want to chat with you. The journey will be rewarding in every sense. Join us and make an impact!

WHAT YOU WILL DO:

• Leadership and Team Management:
• Lead and mentor a team of highly skilled data scientists, fostering a collaborative and innovative environment
• Develop and execute a strategic vision for the AI/ML Data Science team, aligning with company goals
• Ensure the team delivers high-quality predictive models and forecasting solutions that drive business value
• Stakeholder Collaboration and Communication:
• Collaborate with cross-functional teams to understand business needs and translate them into data science projects.
• Communicate complex technical concepts and results to non-technical stakeholders in a clear and actionable manner
• Time Series Forecasting:
• Utilize statistical time series methodologies such as ARIMA and SARIMA, advanced forecasting strategies with deep learning, transformers, and LLMs to develop precise and scalable forecasting models
• Decompose time series data into trend, seasonality, baseline, and error components to improve forecast accuracy
• Experiment with and implement novel approaches to enhance forecasting performance
• Supervised, Unsupervised, and Deep Learning Modeling:
• Apply unsupervised learning techniques for customer segmentation and insights
• Build supervised learning models for classification tasks such as NBA, churn prediction, and cross-sell opportunities
• Develop and deploy deep learning models to solve complex business problems and enhance predictive accuracy
• MLOps and Operationalization:
• Implement MLOps to streamline model development, deployment, and monitoring, enhancing team productivity and efficiency
• Utilize open-source libraries such as MLFlow and Hydra to ensure scalable and reproducible model development
• Advocate for and enforce best practices in data science and machine learning workflows
• Continuous Learning and Innovation:
• Stay up-to-date with the latest trends and advancements in data science, machine learning, and AI
• Encourage continuous learning and development within the team, fostering a culture of innovation