Data Scientist from E-commerce or Fintech Only

Job Description

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.

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