Senior Data Scientist

Overview:

Key Responsibilities:

Lead end-to-end development of predictive, prescriptive, and descriptive models using advanced machine learning and statistical techniques.
Collaborate with business stakeholders to identify opportunities where data science can create measurable business impact.
Build and optimize data pipelines, model training workflows, and MLOps processes for scalable model deployment.
Conduct feature engineering, model validation, and performance tuning to ensure accuracy and reliability.
Guide and mentor junior data scientists and analysts on best practices in modeling, coding, and experimentation.
Present analytical findings and recommendations to senior management in clear, business-friendly language.
Stay updated with the latest trends in AI, LLMs, GenAI, and deep learning technologies to drive innovation.

Required Skills & Experience:

8+ years of experience in Data Science, Machine Learning, or AI-related roles.
Strong expertise in Python, R, or Scala with solid experience using ML libraries (e.g., scikit-learn, TensorFlow, PyTorch, XGBoost).
Proficiency in SQL and working knowledge of big data platforms (e.g., Spark, Databricks, Hadoop).
Hands-on experience with cloud environments (AWS, Azure, or GCP) for model training and deployment.
Strong understanding of statistical modeling, NLP, time series forecasting, and deep learning techniques.
Experience with MLOps tools like MLflow, Kubeflow, or SageMaker.
Excellent problem-solving, communication, and stakeholder management skills.