Senior Data Scientist

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 (e.g., 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. Overview Emmes Group: Building a better future for us all. Emmes Group is transforming the future of clinical research, bringing the promise of new medical discovery closer within reach for patients. Emmes Group was founded as Emmes more than 47 years ago, becoming one of the primary clinical research providers to the US government before expanding into public-private partnerships and commercial biopharma. Emmes has built industry leading capabilities in cell and gene therapy, vaccines and infectious diseases, ophthalmology, rare diseases, and neuroscience. We believe the work we do will have a direct impact on patients lives and act accordingly. We strive to build a collaborative culture at the intersection of being a performance and people driven company. We re looking for talented professionals eager to help advance clinical research as we work to embed innovation into the fabric of our company. If you share our motivations and passion in research, come join us! Primary Purpose We are seeking a talented and motivated Data Scientist to join our team and play a key role in developing innovative AI solutions using the AWS technology stack. You will be responsible for developing, assessing , and fine-tuning various AI and machine learning models and oversee training, testing and deployment of these models. You will work closely with cross-functional teams, including, data engineers, AI engineers, software engineers, DevOps engineers and product managers, to bring AI-powered solutions to life and drive clinical trial acceleration and insights. 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 (e.g., 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. CONNECT WITH US! Follow us on Twitter - @EmmesCRO Find us on LinkedIn - Emmes