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Develop predictive models for business forecasting using AI.
CAI Stack provides cutting-edge pluggable building blocks for the entire AI stack. These components enable enterprises to build and scale machine learning (ML) and deep learning (DL) solutions, addressing diverse use cases. Our platform is utilized by some of the world's largest organizations to deploy vertical-specific products built on our proprietary AI infrastructure.
We are seeking a highly skilled and experienced Senior Data Scientist to join our team. The ideal candidate will be a hands-on expert in developing and deploying deep learning-based forecasting models at scale. You will be responsible for the entire model lifecycle, from ideation and data preparation to model training, deployment, and monitoring in a production environment. This role requires a deep understanding of time series analysis, a passion for building innovative solutions, and the ability to work with large, complex datasets.
Design, develop, and implement state-of-the-art deep learning forecasting models to solve critical business problems.
Lead the entire model development lifecycle, including data cleaning, feature engineering, model selection, training, and validation.
Build scalable and robust forecasting solutions capable of handling massive, high-dimensional time series data.
Select and apply appropriate deep learning architectures for time series, such as LSTMs, GRUs, and Transformers.
Utilize and evaluate various forecasting libraries and frameworks like Neural Forecast, GluonTS, TSAI, TSLib, Merlion, FBProphent, PyTorch Forecasting, Darts, Orbit, statsmodels and others.
Collaborate with data engineers to build and maintain the necessary data pipelines and infrastructure for large-scale model training and deployment.
Perform rigorous model evaluation using appropriate time series-specific techniques like walk-forward validation and backtesting.
Communicate complex technical concepts and model performance to both technical and non-technical stakeholders.
Stay up-to-date with the latest research and advancements in deep learning for time series forecasting.
Hands-On Experience is a Must: Proven track record of building and deploying deep learning models in a production environment. Please be prepared to discuss specific projects and your role in them.
Deep Learning for Time Series: Extensive experience with deep learning architectures tailored for time series data (e.g., LSTMs, GRUs, and especially Transformer-based models like Temporal Fusion Transformer, Autoformer, or PatchTST).
Programming Proficiency: Expert-level proficiency in Python and its data science ecosystem (Pandas, NumPy, Scikit-learn).
Forecasting Libraries: Hands-on experience with at least two of the following libraries:
TSLib
PyTorch Forecasting
Darts
Neuralforecast
GluonTS
TSAI
Merlion
FBProphet
Pytorch Forecasting
Orbit
Specialized Libraries: Hands-on experience with at least two of the following libraries or frameworks:
TorchRec
TensorFlow Recommenders (TFRS)
NVIDIA Merlin
Microsoft Recommenders
NVIDIA Recommenders
Alibaba Recommenders
Frameworks: Strong experience with deep learning frameworks such as PyTorch or TensorFlow.
Scalability: Demonstrated ability to build and train models at scale, including experience with global models and distributed computing.
Statistical and Analytical Skills: Solid understanding of time series statistics, including seasonality, trends, and autocorrelation.
Collaboration & Communication: Excellent communication skills with the ability to work effectively in a cross-functional team.
Advanced degree (M.S. or Ph.D.) in a quantitative field such as Computer Science, Statistics, Mathematics, or a related discipline.
3 years experience in the forecasting domain.
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