Senior Data Scientist

Credence Management Solutions - McLean, VA

Full-time - Mid Level

McLean, VA

Professional, Scientific, and Technical Services

About the position

Credence is seeking a talented and motivated Senior Data Scientist to design, develop, and implement AI models and machine learning algorithms for high-impact projects. This role is ideal for an engineer looking to deepen their expertise and tackle exciting technical challenges.

Responsibilities

• Design, develop, and deploy machine learning models and AI-driven solutions.
• Collaborate with cross-functional teams, including data scientists, software engineers, product managers, and client stakeholders to understand, evaluate, and deliver AI solutions that meet the requirements.
• Conduct data preparation, feature engineering, model selection, training, and optimization to ensure optimal performance from the AI models.
• Design and implement AI solutions using the latest Generative AI technologies and foundation models/large-language models (LLMs).
• Develop automation scripts for MLOps pipelines in the cloud using Infrastructure as Code (IaC) for ML model deployment in model inferencing workflows, following best practices of model versioning and CI/CD deployments.
• Monitor and maintain AI models post-deployment, ensuring performance, accuracy, and scalability.
• Contribute to the development of AI tools, frameworks, and best practices to support the company's AI initiatives.
• Stay up-to-date with emerging trends, tools, and techniques in AI and machine learning.
• Write clean, maintainable, and well-documented code following industry standards.

Requirements

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• 3-5 years of hands-on experience in AI/ML development in a professional working environment, with a track record of successful AI project deployments.
• Strong understanding of supervised, unsupervised, and reinforcement learning techniques.
• Proficiency in using Python and common AI/ML libraries and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn, NumPy, Pandas) for data preprocessing, feature engineering, and model development techniques.
• Basic understanding and usage of foundation models/large language models (LLMs), vector embeddings, and their application.
• Experience with cloud platforms (AWS, Google Cloud Platform, or Azure) and containerization tools (Docker, Kubernetes).
• Experience using ML frameworks and tools in the cloud, such as Amazon Sagemaker.
• Strong problem-solving skills and the ability to work both independently and as part of a team.
• Excellent communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.

Nice-to-haves

• Experience with Natural Language Processing (NLP).
• Experience using advanced search and retrieval techniques like retrieval augmented generation (RAG) for Large Language Models (LLMs).
• Experience with fine-tuning of Large Language Models with custom datasets.
• Familiarity with MLOps principles and tools such as MLflow.
• Knowledge of software development best practices, including version control (Git) and CI/CD pipelines.