Data Scientist - AI Practice Team

• Lead the preparation, exploration, and analysis of client data (tabular, time-series, and document-based) to enable robust modeling, feature engineering, and insight generation.
• Design, implement, and validate machine learning models and analytics pipelines, including problem framing, model selection, evaluation, and iteration for real-world performance.
• Drive advanced use of NLP and document understanding techniques to extract, transform, and enrich information from reports, PDFs, logs, and other unstructured sources.
• Build and maintain clear, impactful dashboards, reports, and visualizations (e.g., in Python, Power BI, or similar tools) to communicate findings to consultants and client stakeholders.
• Collaborate with consultants and domain experts to translate business problems into analytical solutions, articulate trade-offs, and present recommendations to technical and non-technical audiences.
• Ensure technical quality, reproducibility, and governance by establishing good practices for code, documentation, data management, and model tracking across projects.
• Mentor and support junior data scientists, providing guidance on methods, tooling, and best practices, and reviewing their work for quality and consistency.

• Bachelor’s degree in a STEM discipline (e.g., Data Science, Computer Science, Engineering, Mathematics, Statistics) or related field; Master’s degree preferred or equivalent experience.
• 5+ years of experience applying data science and machine learning in professional settings, including end-to-end delivery of analytics/ML solutions.
• Proven track record working with real-world, messy datasets (including unstructured/document data) across the full lifecycle: data preparation, modeling, evaluation, and deployment handoff.
• Experience leading or owning significant workstreams within AI/ML or analytics projects, ideally in consulting, industrial, or asset-intensive environments.
• Practical experience working with cloud-based and modern data platforms (e.g., Azure, AWS, GCP, Databricks) and integrating with enterprise data sources and workflows.

Knowledge, Skills, and Abilities

• Deep proficiency in Python for data science (pandas, scikit-learn, and related libraries) and strong SQL skills for working with relational and analytical data stores.
• Strong grounding in statistics, machine learning, and model evaluation, including supervised/unsupervised methods, feature engineering, and performance diagnostics.
• Hands-on experience with NLP and document understanding (e.g., text preprocessing, embeddings, classification, information extraction, transformers/LLMs) applied to real datasets.
• Ability to design and implement robust, maintainable analytics and ML pipelines, using notebooks and production-ready code with Git-based version control.
• Familiarity with modern data and ML tooling (e.g., Databricks, MLflow, Docker, CI/CD for data/ML) and good practices for experiment tracking and reproducibility.
• Proficiency with BI/visualization tools (e.g., Power BI, Tableau) and data storytelling skills to communicate complex analytical results to non-technical stakeholders.
• Excellent communication and stakeholder engagement skills, with the ability to frame analytical approaches, explain trade-offs, and align solutions with business objectives.
• Proven ability to work across multiple projects, manage priorities, and operate in a fast-moving, consulting-style environment, while mentoring junior team members.
• Nice to have: exposure to industrial, maritime, or asset-intensive domains, or prior experience in AI consulting or client-facing roles., Amazon Web Services (AWS), Analysis Skills, Artificial Intelligence (AI), Best Practices, Business Intelligence, Cloud Computing, Communication Skills, Computer Science, Consulting, Continuous Deployment/Delivery, Continuous Integration, Customer Relations, Customer/Client Research, Data Analysis, Data Management, Data Modeling, Data Science, Data Sets, Docker, Documentation, Enterprise Data Integration, GCP (Good Clinical Practices), Git, Leadership, Machine Learning, Machine Tool, Management Strategy, Mathematics, Mentoring, Microsoft Windows Azure, Model Validation, Multitasking, Natural Language Processing (NLP), People Management, Power BI, Project Tracking, Python Programming/Scripting Language, Reporting Dashboards, SQL (Structured Query Language), Source Code/Configuration Management (SCM), Statistics, Storytelling, Tableau, Technical Presentation, Unstructured Data

$100,000 - $150,000 USD

Starting salary is based on multiple factors including skillset and experience.

Notice: This role has been opened for a future need expected within three months of the original posting date of this position. Potential candidates may not receive communication until the open headcount is confirmed.