Data Scientist from E-commerce or Fintech Only

personalisationmodellingKey Responsibilities

• Lead the end-to-end design, development, and deployment of machine learning/statistical models for real-world business applications.
  
• Collaborate with Product, Engineering, and Business teams to understand needs and translate them into data science problems.
  
• Build and productionize models using Python, SQL, and PySpark on large-scale datasets.
  
• Apply deep learning and advanced statistical techniques for tasks such as classification, regression, recommendation, and personalization.
  
• Perform data wrangling, feature engineering, model tuning, and evaluation.
  
• Monitor and manage ML model lifecycle using tools like

  MLflow

  and

  Airflow

  .
• Communicate technical insights and findings effectively to both technical and non-technical stakeholders.
  
• Stay updated on the latest trends in machine learning and AI, and integrate relevant advancements.
  

Must-Have Skills & Qualifications

• 6+ years of hands-on experience in data science.
  
• Strong Python skills, including libraries such as

  NumPy

  ,

  pandas

  ,

  scikit-learn

  ,

  TensorFlow/PyTorch

  .
• Proficiency in SQL and experience with

  PySpark

  or distributed data processing.
• Deep understanding of ML/DL algorithms, model development workflows, and statistical modeling.
  
• Proven ability to deploy and manage production-grade models at scale.
  

Good-to-Have Skills

• Experience with

  NLP

  ,

  time series forecasting

  , or

  unsupervised learning

  techniques.
• Familiarity with

  MLOps tools

  : MLflow, Airflow, Docker, Kubernetes.
• Exposure to

  data engineering principles

  , including batch/stream processing.
• Experience deploying ML APIs using

  FastAPI

  or similar frameworks.
• Knowledge of tools like

  Streamlit

  ,

  Tableau

  , and version control systems (e.g., Git).
• Strong problem-solving skills and the ability to explain complex concepts clearly.
  

Preferred Qualifications

• Masters degree in Computer Science, AI, or a related technical field.
  
• Experience in prompt engineering and fine-tuning large language models (LLMs).
  
• Familiarity with model governance and versioning practices.
  
• Contributions to open-source projects or published research in data science/AI domains.