[Remote] Senior Data Scientist

About the role

FlexBoard is building an AI Lab to integrate machine learning and artificial intelligence across its operations. This role will contribute to the research, development, and implementation of AI/ML solutions designed to boost efficiency and analytical power within the organization.

Key facts

What you'll do

• Develop and refine machine learning and artificial intelligence solutions, focusing on practical applications.
• Create and test initial AI concepts that demonstrate value for specific business needs, moving successful prototypes into production.
• Design and build applications that use large language models for tasks like text analysis, summarization, and information extraction, incorporating techniques such as retrieval-augmented generation.
• Experiment with model customization and evaluation to improve AI solution performance for FlexBoard's use cases.
• Assess new AI technologies and frameworks to find opportunities for adoption.
• Apply advanced statistical and machine learning methods, including supervised and unsupervised learning, classification, and deep learning.
• Build, deploy, and maintain AI/ML models and applications within cloud environments (AWS, Kubernetes) or internal platforms, collaborating with AI Engineers or managing deployment independently.
• Develop interactive dashboards and analytical applications using Python frameworks like Streamlit, Dash, or Flask, or R Shiny, and utilize AI-assisted development tools for rapid prototyping.
• Create data visualizations and user interfaces using Python libraries (Plotly, Matplotlib, Seaborn), R (ggplot2), Tableau, or Power BI to present analytical findings clearly to non-technical audiences.
• Manage deployment pipelines, including containerization with Docker, CI/CD practices, and GenAI application deployments with API integrations, rate limiting, and cost management.
• Implement monitoring, logging, and alerting for model performance, data quality, and system health, establishing automated retraining and model versioning.
• Troubleshoot and maintain deployed applications, addressing performance, ensuring scalability, and updating as requirements evolve.
• Support documentation needs for assessments, reviews, and compliance obligations.
• Participate in agile development cycles, including sprint planning, daily stand-ups, and retrospectives.
• Break down technical work into manageable tasks, estimate effort, and communicate progress, challenges, and technical details to stakeholders.
• Collaborate with business stakeholders to understand needs, identify AI/ML opportunities, and translate requirements into technical solutions.
• Communicate technical concepts effectively to both technical and non-technical audiences through presentations and reports.
• Document technical work, methodologies, and project outcomes to facilitate knowledge sharing.
• Contribute to building the AI/ML team's capabilities through documentation and mentoring.

Requirements

• At least six years of practical experience in developing, deploying, and maintaining AI/ML applications in a large or complex organization.
• Bachelor's degree in Computer Science, Data Science, Statistics, Machine Learning, or a related technical field.
• Expert ability in Python or R for data science development.
• Experience with production deployment of AI/ML applications.
• Proven ability to build, deploy, and maintain AI/ML applications in cloud environments, including containerization and basic CI/CD.
• Experience building interactive applications and dashboards using frameworks like Streamlit, Dash, Flask, or R Shiny.
• Strong experience creating visualizations and dashboards using Python/R libraries, Tableau, or Power BI to communicate technical concepts to non-technical audiences.
• Advanced knowledge of machine learning, natural language processing (NLP), and generative AI technologies.
• Advanced knowledge of statistical modeling, data analysis techniques, and problem-solving.
• Ability to work independently and collaboratively, taking ownership of solutions from concept to deployment.

Nice to have

• Master's degree in Computer Science, Data Science, Statistics, Machine Learning, or a related technical field.
• Prior experience in U.S. federal government, regulatory, supervisory, or policy environments.
• Experience with financial services data, consumer finance, banking supervision, or regulatory data.
• Experience working within agile frameworks (Scrum, Kanban) and project tracking tools (Jira, Azure DevOps).
• Experience with large language model platforms (e.g., GPT, Llama) and frameworks (e.g., LangChain, LlamaIndex); knowledge of prompt engineering, fine-tuning, vector databases, and semantic search.
• Familiarity with AWS AI services (e.g., Amazon Bedrock, SageMaker, Comprehend, Rekognition, Transcribe).
• Experience building production-grade web applications with advanced user interfaces; knowledge of data storytelling and visual design principles.
• Experience visualizing model performance metrics, feature importance, and model explainability outputs.
• Hands-on experience with AWS deployment services (e.g., EC2, EKS, Lambda, S3, CloudWatch), Docker, and infrastructure as code (Terraform, CloudFormation).
• AWS certifications (e.g., Solutions Architect, Machine Learning Specialty).
• Familiarity with MLOps practices including model monitoring, versioning, automated retraining, and deployment pipelines.
• Experience with multi-modal AI applications; understanding of responsible AI practices (bias detection, fairness evaluation, model interpretability).
• Familiarity with federal IT governance frameworks (e.g., FISMA, NIST requirements) and application security in regulated environments.
• Experience working with sensitive or regulated data.

Skills & tools

• Python
• R
• Streamlit
• Dash
• Flask
• R Shiny
• Plotly
• Matplotlib
• Seaborn
• ggplot2
• Tableau
• Power BI
• Scikit-learn
• Spacy
• XGBoost
• Docker
• Kubernetes
• AWS
• LangChain
• LlamaIndex
• Terraform
• CloudFormation

Practical notes

• U.S. citizenship required.
• This is a remote position for candidates located in the USA.