Senior Data Scientist, Deep Learning Forecasting

1 week ago

Bengaluru, Karnataka, India Couture Full time ₹ 15,00,000 - ₹ 30,00,000 per year

Summary:

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.

Responsibilities:

  • 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
    ,
    PyTorch Forecasting
    ,
    Darts
    ,
    Orbit
    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.

Required Qualifications:

  • Hands-On Experience ON LARGE DATASETS 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
- 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.

Preferred Qualifications:

  • 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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