Manager, AI Engineering

About the Role

Anaplan is seeking an expert AI Tech Lead to join our Forecaster team, the core group responsible for developing the machine learning algorithms that power our platform's forecasting and predictive capabilities. In this role, you will be the technical authority for a team dedicated to solving one of the most critical challenges in business planning: time series forecasting. You will lead the design and implementation of cutting-edge forecasting models, from classical statistical methods to state-of-the-art deep learning architectures. As a hands-on leader, you will guide the team in building robust, scalable, and accurate forecasting solutions that our customers rely on to make multi-billion dollar decisions.

Key Responsibilities

• Lead the technical design, development, and deployment of high-performance time series forecasting algorithms.
• Provide hands-on coding and architectural leadership, establishing best practices for building and validating forecasting models.
• Mentor a team of AI/ML engineers and developers, fostering deep expertise in forecasting techniques and elevating the team's technical capabilities.
• Drive the research and implementation of state-of-the-art forecasting algorithms to continuously improve model accuracy and performance.
• Own the end-to-end lifecycle of forecasting models, including MLOps, CI/CD, deployment, and monitoring in a production cloud environment.
• Collaborate with product managers and researchers to define the technical roadmap for the Forecaster team.
• Lead by example through rigorous code reviews, technical deep dives, and a commitment to engineering excellence.

 

Required Qualifications

• Deep Expertise in Time Series Forecasting Algorithms
• Extensive hands-on experience with a wide range of models, including:
• Classical methods (e.g., ARIMA, SARIMA, ETS).
• Modern statistical models (e.g., Prophet).
• Advanced deep learning architectures (e.g., LSTMs, GRUs, CNNs, DeepAR and Transformer-based models like N-BEATS).
• Strong programming skills in Python and deep familiarity with:
• Core machine learning libraries: scikit-learn, Pandas, NumPy.
• Deep learning frameworks: TensorFlow or PyTorch.
• Forecasting-specific libraries: statsmodels, pmdarima, or Darts.
• Proven track record of leading technical projects and shipping production-grade machine learning systems.
• Solid experience with cloud platforms (AWS, Azure, or GCP) and deploying ML models at scale.
• Strong understanding of modern software engineering principles (testing, CI/CD, version control) and MLOps.

Preferred Qualifications

• Master’s or Ph.D. in a quantitative field like Computer Science, Engineering or equivalent.
• Experience with probabilistic forecasting and quantifying uncertainty