Data Scientist-Deep Learning (Financial Services Domain)

Data Scientist – Deep Learning (Financial Services Domain)

Data Scientist

Key Responsibilities:

• Design, implement, and deploy deep learning models for financial use cases, including stock price prediction, risk scoring, and algorithmic trading.
• Optimize models for performance and scalability through techniques such as quantization, pruning, and efficient architecture design.
• Rigorously evaluate models using appropriate metrics, validation techniques, and hyperparameter tuning, while maintaining robust experiment tracking practices.
• Deploy and maintain machine learning workflows using

  MLOps best practices

  on cloud platforms (AWS preferred).
• Conduct exploratory data analysis (EDA), feature engineering, and time-series analysis on structured and unstructured financial datasets.
• Apply advanced pattern recognition techniques to derive insights and predictive signals from data.
• Stay abreast of the latest research in deep learning, including advancements in LLMs, attention mechanisms, and financial NLP.
• Collaborate with cross-functional teams, including quantitative analysts, fellow data scientists, and product managers, to translate business requirements into technical solutions.
• Ensure high-quality code, documentation, and backtesting processes.

Required Qualifications:

• Ph.D. or Master’s degree in Computer Science, Machine Learning, Statistics, Mathematics, or a related discipline from a reputed institute (IITs or BITS preferred).
• 5-7 years of experience in developing and deploying deep learning or AI models, ideally within the financial services industry.
• Hands-on experience with

  PyTorch or TensorFlow

  , and Python data libraries such as

  pandas, NumPy, and scikit-learn

  .
• Strong background in predictive modeling, anomaly detection, and pattern recognition using financial data.
• Proficient in Python or PySpark for large-scale data processing.
• Ability to work with diverse financial datasets, including stock prices, order books, financial statements, and alternative data sources.

Preferred Qualifications:

• Experience with cloud platforms (AWS, GCP, or Azure) for model training, deployment, and scaling.
• Familiarity with ML pipelines, Docker, and modern MLOps frameworks.
• Exposure to Natural Language Processing in finance (e.g., sentiment analysis, earnings call transcripts).
• Working knowledge of core financial concepts such as P&L, return metrics, technical indicators, and risk measurement.