Data Science Architect

Job Description

Role & responsibilities Architect and lead the development of machine learning solutions, aligning with business objectives and strategic initiatives. Oversee a team of data scientists and engineers, providing technical guidance and mentorship. Develop and implement advanced machine learning models, including deep learning, time series forecasting, and natural language processing. Collaborate with cross-functional teams to identify opportunities for ML-driven solutions and translate business requirements into technical specifications. Build and maintain robust ML pipelines, incorporating data ingestion, preprocessing, feature engineering, model training, evaluation, deployment, and monitoring. Leverage cloud platforms (e.g., Azure) for efficient ML model development, training, and deployment. Drive the adoption of MLOps practices to improve model lifecycle management and operational efficiency. Utilize data visualization tools (Superset, Power BI) to communicate insights and drive data-informed decision making. Experience in developing Analytics, Machine learning offerings end to end including training models, serving models using standard frameworks like TF/TFX/Pytorch/Azure AIML, deployments at scale Embeddings and Vector Space Model Expertise: Good knowledge of embedding techniques and vector space models as they apply to generative AI. This includes creating, tuning, and leveraging embeddings for various types of data (text, images, etc.). Large Language Model Proficiency: Experience in working with LLMs, particularly in their application to embeddings and vector space models. Ability to fine-tune these models for specific applications. Preferred candidate profile Advanced degree in Computer Science, Statistics, or a related field. 10-12+ years of experience in machine learning and data science. Proven experience in architecting and implementing large-scale ML systems. Strong proficiency in Python and related ML libraries (TensorFlow, PyTorch, Scikit-learn). Expertise in data engineering, including data extraction, transformation, and loading (ETL) processes. Experience with cloud platforms (Azure preferred), big data technologies, and containerization. Demonstrated ability to lead and mentor data science teams. Excellent communication and interpersonal skills.