Lead design, development, and deployment of scalable data science solutions optimizing large-scale data pipelines in collaboration with engineering teams.Architect advanced machine learning models (deep learning, RL, ensemble) and apply statistical analysis for business insights.Apply statistical analysis, predictive modeling, and optimization techniques to derive actionable business insights.Own the full lifecycle of data science projects—from data acquisition, preprocessing, and exploratory data analysis (EDA) to model development, deployment, and monitoring.Implement MLOps workflows (model training, deployment, versioning, monitoring) and conduct A / B testing to validate models.Required Skills :Expert in Python, data science libraries (Pandas, NumPy, Scikit-learn), and R.Extensive experience with machine learning (XGBoost, PyTorch, TensorFlow) and statistical modeling.Proficient in building scalable data pipelines (Apache Spark, Dask) and cloud platforms (AWS, GCP, Azure).Expertise in MLOps (Docker, Kubernetes, MLflow, CI / CD).Strong data visualization skills (Tableau, Plotly Dash) and business acumen.Nice to Have :Experience with NLP, computer vision, recommendation systems, or real-time data processing (Kafka, Flink).Knowledge of data privacy regulations (GDPR, CCPA) and ethical AI practices.Contributions to open-source projects or published research.Skills Required
Machine Learning, MLops, Statistical Modeling, Python