Senior Data Scientist

Responsibilities

• ML Model Development and Deployment: Leverage state-of-the-art deep learning techniques to build scalable and high-performance solutions that drive innovation and efficiency

• Utilize advanced architectures such as Long Short-Term Memory (LSTM) networks, Large Language Models (LLMs) and transformer-based models (eg, BERT, GPT, T5) for tasks like text generation, sequence modeling, entity recognition and context-aware recommendations
• Evaluate problem requirements and data features to select and implement the most suitable AI methodologies, including self-learning, reinforcement learning or other advanced techniques
• Ensure chosen approach aligns with business objectives while balancing model accuracy, interpretability and computational efficiency
• Train, fine-tune and evaluate AI/ML models using AWS services such as Amazon SageMaker, Amazon Bedrock and ML Compute to ensure scalability, robustness and cost-effectiveness in cloud-based environments
• Collaborate with engineering teams to integrate and operationalize AI models into production systems, enabling real-time predictions and data-driven decision-making while ensuring reliability, scalability and low-latency performance
• Data Expertise and Infrastructure: Analyze, process and extract actionable insights from complex datasets stored across AWS services such as Amazon S3, Redshift, RDS and DynamoDB
• Utilize SQL, Python and other relevant programming languages to perform data wrangling, transformation and feature engineering to support AI model development
• Explore and visualize large-scale datasets to identify patterns, trends, correlations and anomalies that can enhance AI model training and decision-making
• Leverage statistical analysis, data mining techniques and domain knowledge to improve data-driven insights
• Collaborate with data engineers and AI engineers to design and optimize scalable data pipelines and infrastructure
• Ensure seamless data ingestion, processing and integration for AI models, enhancing efficiency, automation and real-time capabilities
• Collaboration and Communication: Communicate the insights and implications of your AI models to stakeholders in a clear and concise manner, bridging the gap between technical expertise and clinicians
• Stay up-to-date on the latest advancements in AI research and translate them into practical applications for the company
• Qualifications 4+ years of experience as a Data Scientist or similar role, with a strong focus on AI and machine learning
• Proven experience in designing and implementing production-grade data-driven AI models
• In-depth understanding of deep learning, self learning, reinforcement learning or other relevant AI techniques
• Expertise in Python and familiarity with AI libraries such as TensorFlow, PyTorch, Scikit-Learn, etc
• Strong understanding of probability theory, statistical analysis and machine learning methods
• Excellent communication and collaboration skills