Senior Data Scientist, Deep Learning Forecasting

Couture; India; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

A technology company in India is seeking a Senior Data Scientist specializing in deep learning for business forecasting. The ideal candidate will develop and deploy advanced forecasting models, utilizing large datasets and deep learning architectures like LSTMs and Transformers. A proven track record in production environments, expert Python skills, and experience with relevant libraries such as PyTorch and TSLib are essential. The role is full-time and on-site, offering the opportunity to work with cutting-edge AI infrastructure.

Qualifications

• Proven track record of building and deploying deep learning models.
• Expert-level proficiency in Python and data science libraries.
• Hands-on experience with forecasting libraries.

Responsibilities

• Design and implement deep learning forecasting models.
• Lead model development lifecycle including training and validation.
• Collaborate with data engineers for model deployment.

Skills

Deep learning models

Time series analysis

Python

Pandas

Scikit-learn

Communication skills

Education

Advanced degree in quantitative field

Tools

PyTorch

TensorFlow

GluonTS

TSLib

Job description

Join our mission to deliver AI infrastructure at scale. Explore opportunities to work with advanced middleware tech stacks.

Senior Data Scientist, Deep Learning Forecasting

Develop predictive models for business forecasting using AI.

On-site

Full-Time

About Couture AI

Couture AI 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.

Job Description

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.

Key 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, 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.

Required Qualifications

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.

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
• GluonTS
• TSAI
• Orbit

Specialized Libraries: Hands‑on experience with at least two of the following libraries or frameworks:

• TorchRec
• TensorFlow Recommenders (TFRS)
• NVIDIA Merlin

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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