Junior Data Scientist AI Practice Team

What you'll work on

• Support AI consulting engagements by cleaning, structuring, and analyzing client data (tabular, time-series, and document-based) to enable modeling and insight generation.
• Contribute to development, testing, and documentation of machine learning models, analytics pipelines, and proof-of-concept solutions under guidance from senior data scientists.
• Work with our document and data services to extract, transform, and enrich information from reports, PDFs, logs, and other unstructured sources using NLP and related techniques.
• Build and maintain basic 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 questions into analytical tasks, validate results, and refine approaches based on feedback.
• Help maintain clean, reproducible project assets (code, notebooks, datasets, documentation) using modern collaboration and version control tools.

What we're looking for

Education and Experience

• Bachelor’s degree in a STEM discipline (e.g., Data Science, Computer Science, Engineering, Mathematics, Statistics) or related field, or equivalent practical experience.
• 2+ years of combined experience through projects, internships, or professional roles applying data science/ML methods and tools.
• Practical experience applying core techniques in data preprocessing, modeling, and evaluation using Python, SQL, and common ML libraries.
• Exposure to AI/ML or analytics projects in academic, research, or professional environments, ideally with real-world or messy datasets.
• Familiarity with cloud-based and modern data platforms (e.g., Azure, AWS, GCP, Databricks) and BI tools is a plus but not mandatory.

Knowledge, Skills, and Abilities

• Strong foundation in data science/ML concepts and statistics, with hands-on experience in Python (e.g., pandas, scikit-learn) and working with SQL-based data sources.
• Ability to clean, structure, and analyze real-world datasets, including unstructured or semi-structured data (e.g., documents, logs, text).
• Comfortable working with Jupyter notebooks and Git-based workflows for reproducible and version-controlled analysis.
• Clear, structured communication skills, including the ability to explain analytical work and findings to non-technical stakeholders in a concise, business-relevant way.
• Collaborative mindset and willingness to learn, taking feedback from senior team members and adapting quickly to new tools, methods, and domains.
• Organized, detail-oriented working style, with the ability to manage tasks across multiple projects and meet deadlines reliably.

• Experience applying ML/NLP to real datasets (e.g., classification, forecasting, document information extraction, OCR, LLMs, or search/retrieval systems).
• Any exposure to industrial, maritime, or asset-intensive domains, or to consulting/client-facing environments.