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2 Days ago

Senior Data Scientist

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Information Technology
Full-Time
BharatPe

Overview

Job Title: Senior Data Scientist (ML/Deep Learning)

Location: Gurgaon

Employment Type: Full-time

Department: Data Science / A3

Job Description:

We are seeking a highly motivated Data Scientist with 3-5 years of experience in developing and deploying AI, deep learning and machine learning models. You will support the team in building predictive models, deploying them via APIs, and applying modern AI techniques like Transformers and LLMs. Exposure to MLOps, Git, and cloud platforms is a plus. This role offers hands-on learning in a fast-paced, real-world environment.

Key Responsibilities:

  • Experience of deploying at least 1 AI/Ml project into production.
  • Experience with Generative AI concepts and tools, including transformer-based models (e.g., LLaMA, GPT) and frameworks like LangChain and LangGraph.
  • Strong grasp of core machine learning algorithms, including statistical analysis, linear/logistic regression, tree-based models (e.g., Random Forest, XGBoost), and unsupervised learning techniques such as clustering
  • Knowledge of implementing deep learning models such as CNNs, RNNs, and Transformers using PyTorch or TensorFlow for NLP and computer vision tasks.
  • Skilled in developing and deploying scalable AI solutions using FastAPI and Docker, leveraging Generative AI for real-time insights and data processing.
  • Basic understanding of cloud platforms such as AWS and GCP for hosting and scaling machine learning models.
  • Proficient in Python and data libraries like Pandas and NumPy, with working knowledge of SQL for data extraction and preprocessing.
  • Familiarity with MLOps fundamentals is a plus.

Required Qualifications:

  • Bachelor’s or Master's Degree in a quantitative field (Computer Science, Statistics, Economics, Mathematics, Operations Research, Analytics, or a related quantitative field)
  • 3-5 years of practical experience working on end-to-end machine learning and deep learning projects, including model development, evaluation, and deployment.
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