Data Scientist/AI

Role Overview: As a Data Scientist at our company, your main responsibilities will include:

Key Responsibilities: - Design, build, and optimize supervised, unsupervised, and deep learning models for various business problems. - Clean, transform, and analyze structured and unstructured data to identify significant features. - Develop end-to-end AI pipelines and integrate models into production systems using APIs or deployment frameworks. - Collaborate closely with product managers, domain experts, and engineers to understand requirements and translate them into data science solutions. - Track model performance metrics such as RMSE, F1 score, and ROC AUC, and prepare technical documentation and reports. - Stay up to date with the latest AI/ML techniques and propose innovative applications.

Qualifications Required: - Bachelors or Masters degree in Computer Science, Statistics, Mathematics, Data Science, or a related field. - 4-6 years of hands-on experience in Python, R, SQL, Machine Learning frameworks (e.g., Scikit-learn, XGBoost, LightGBM), Deep Learning (e.g., TensorFlow, PyTorch, Keras), Data manipulation and visualization (e.g., Pandas, NumPy, Matplotlib, Seaborn), Model deployment tools (e.g., Flask, FastAPI, Docker, MLflow), cloud platforms (AWS/GCP/Azure), and MLOps tools. - Familiarity with large-scale data processing (e.g., Spark, Dask) is a plus. - Exposure to NLP, computer vision, or generative AI (OpenAI, Hugging Face, LangChain) is highly desirable. - Strong analytical and communication skills. - Nice to Have: Experience in industry-specific AI applications (e.g., fintech, healthcare, retail), contribution to open-source projects or ML competitions (e.g., Kaggle), certifications in Data Science / AI from recognized platforms. Role Overview: As a Data Scientist at our company, your main responsibilities will include:

Key Responsibilities: - Design, build, and optimize supervised, unsupervised, and deep learning models for various business problems. - Clean, transform, and analyze structured and unstructured data to identify significant features. - Develop end-to-end AI pipelines and integrate models into production systems using APIs or deployment frameworks. - Collaborate closely with product managers, domain experts, and engineers to understand requirements and translate them into data science solutions. - Track model performance metrics such as RMSE, F1 score, and ROC AUC, and prepare technical documentation and reports. - Stay up to date with the latest AI/ML techniques and propose innovative applications.

Qualifications Required: - Bachelors or Masters degree in Computer Science, Statistics, Mathematics, Data Science, or a related field. - 4-6 years of hands-on experience in Python, R, SQL, Machine Learning frameworks (e.g., Scikit-learn, XGBoost, LightGBM), Deep Learning (e.g., TensorFlow, PyTorch, Keras), Data manipulation and visualization (e.g., Pandas, NumPy, Matplotlib, Seaborn), Model deployment tools (e.g., Flask, FastAPI, Docker, MLflow), cloud platforms (AWS/GCP/Azure), and MLOps tools. - Familiarity with large-scale data processing (e.g., Spark, Dask) is a plus. - Exposure to NLP, computer vision, or generative AI (OpenAI, Hugging Face, LangChain) is highly desirable. - Strong analytical and communication skills. - Nice to Have: Experience in industry-specific AI applications (e.g., fintech, healthcare, retail), contribution to open-source projects or ML competitions (e.g., Kaggle), certifications in Data Science / AI from recognized platforms.