Join CAI Stack to advance AI infrastructure at scale. Work with cutting-edge middleware tech and innovative forecasting solutions.
Create predictive models to transform business forecasting using AI.
CAI Stack delivers modular AI infrastructure that enables enterprises to build, scale, and deploy advanced ML and DL solutions. Our platform powers vertical-specific AI applications for some of the world’s leading organizations, helping them achieve faster, more accurate business decisions.
We are looking for a Senior Data Scientist to drive deep learning-based forecasting initiatives. This role involves designing, training, and deploying predictive models capable of handling complex, high-dimensional time series data. You will oversee the entire lifecycle of forecasting models, ensuring robustness, scalability, and actionable insights for business stakeholders.
Develop state-of-the-art deep learning models for business forecasting.
Lead end-to-end model development including data preprocessing, feature engineering, architecture selection, training, and evaluation.
Design scalable solutions for large, multi-dimensional time series datasets.
Select and implement advanced architectures such as LSTMs, GRUs, and Transformer-based models.
Work with libraries and frameworks like Neural Forecast, GluonTS, TSAI, TSLib, Merlion, FBProphet, PyTorch Forecasting, Darts, Orbit, and statsmodels.
Collaborate with data engineers to build pipelines and infrastructure for efficient model training and deployment.
Apply rigorous evaluation methods including walk-forward validation and backtesting.
Effectively communicate model insights and results to technical and non-technical stakeholders.
Stay current with research and advancements in deep learning for time series forecasting.
Proven experience in building and deploying deep learning forecasting models in production.
Strong expertise in time series architectures such as LSTMs, GRUs, and Transformer-based models (e.g., Temporal Fusion Transformer, Autoformer, PatchTST).
Advanced proficiency in Python and relevant data science libraries (Pandas, NumPy, Scikit-learn).
Hands-on experience with at least two of these forecasting libraries:
TSLib
PyTorch Forecasting
Darts
Neuralforecast
GluonTS
TSAI
Merlion
FBProphet
Orbit
Experience with recommendation or specialized ML libraries (at least two):
TorchRec
TensorFlow Recommenders (TFRS)
NVIDIA Merlin
Microsoft Recommenders
NVIDIA Recommenders
Alibaba Recommenders
Expertise in deep learning frameworks such as PyTorch or TensorFlow.
Experience scaling models using distributed computing and global model strategies.
Strong understanding of statistical principles for time series data including trends, seasonality, and autocorrelation.
Excellent communication and teamwork skills for cross-functional collaboration.
Advanced degree (M.S. or Ph.D.) in Computer Science, Statistics, Mathematics, or related quantitative field.
At least 3 years of experience in the forecasting domain.
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