Data Scientist - Kandivali

Exp - 3+
Budget - 10

Location - Mumbai,Kandivali onsite

Pref - male candidates

"Job Overview
We are looking for an experienced Data Scientist (ML) to build and deploy intelligent systems using machine learning, deep learning, and GenAI techniques. The role focuses on production-grade AI solutions, not just model building.
Responsibilities
Develop and deploy machine learning and deep learning models.
Work on data preprocessing, feature engineering, and model training.
Perform feature selection and hyperparameter tuning to optimize model performance.
Build and optimize end-to-end ML pipelines.
Integrate ML models into applications using Python APIs (FastAPI/Flask).
Work with large datasets using SQL and Python.
Evaluate models using appropriate metrics and improve their accuracy, scalability, and reliability.
Monitor model performance in production, identify model drift, and implement retraining strategies.
Collaborate with backend and product teams for AI integration.
Work on GenAI / LLM-based solutions (optional but preferred).
Required Skills
Solid in Python programming.
Machine Learning: Regression,



Classification, Clustering, Ensemble Learning (Random Forest, XGBoost, LightGBM).
Strong understanding of feature engineering, feature selection, and hyperparameter tuning.
Proficiency in model evaluation techniques including Accuracy, Precision, Recall, F1-Score, ROC-AUC, and Cross Validation.
Libraries: Scikit-learn, Pandas, NumPy.
Basic Deep Learning: TensorFlow or PyTorch (good to have).
Strong SQL and data handling skills.
Understanding of model deployment and production monitoring.
Experience building end-to-end ML pipelines from data preprocessing to deployment.
Valuable to Have
Experience with LLMs / GenAI (OpenAI, LangChain, Hugging Face).
Knowledge of MLOps (Docker, Kubernetes, MLflow).
Cloud platforms (AWS / Azure / GCP).
Experience in API development (FastAPI / Flask).
Must Have Mindset
Robust problem-solving and analytical thinking.
Production-focused (not only research work).
Ability to handle real-world messy data.
Ownership of end-to-end AI solutions."

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