Data Scientist-1
Viridien (www.viridiengroup.com) is an advanced technology, digital and Earth data company that pushes the boundaries of science for a more prosperous and sustainable future. With our ingenuity, drive and deep curiosity we discover new insights, innovations, and solutions that efficiently and responsibly resolve complex natural resource, digital, energy transition and infrastructure challenges.
Key Responsibilities
Develop systematic data-transformation modules to gather, clean, validate, and structure raw datasets.
Collaborate closely with Subject Matter Experts (SMEs) and the labeling team, building a solid understanding of domain knowledge and requirements.
Support SMEs and the labeling team by providing tools, solutions, and continuous technical feedback to improve annotation workflows.
Build scalable, reusable data-processing solutions and maintain version control using GitLab.
Troubleshoot and diagnose data issues, proactively flagging inconsistencies or risks.
Maintain a deep understanding of the Data Hub technology stack and data schemas, staying current with emerging technologies, models, and research.
Contribute to broader initiatives related to data integration, feature design, and machine learning.
Design and run experiments, iterate based on results, and document and share learnings with the team.
Use pre-trained ML models, and train or fine-tune models on internal datasets with proper evaluation and validation.
Communicate regularly with team members, the team lead, and cross-functional partners in production and technology to ensure alignment and transparency.
Performance Metrics
Delivers high-quality work on time.
Quickly learns and applies new tools, technologies, and concepts.
Demonstrates strong understanding of Data Hub systems and workflows.
Qualifications & Skills
Background in data science or a related field (Master’s degree preferred).
Proficient in at least one programming language—ideally Python—with experience in common ML libraries.
Experience with hybrid ML workflows (traditional ML, LLMs, embeddings, ontologies, knowledge graphs).
Comfortable working with relational, NoSQL, and graph databases.
Strong data-processing skills, including cleaning, filtering, and feature extraction.
Clear communicator with strong collaboration and presentation skills.
Our Hiring Process
At Viridien, we are committed to delivering a respectful, inclusive, and transparent recruitment experience.
Due to the high volume of applications we receive, we may not be able to provide individual feedback to every applicant. Only candidates whose qualifications closely match the role criteria will be contacted for an interview. We do, however, aim to share personalized feedback with those who progress to the first round of interviews and beyond.
We are also dedicated to ensuring that our hiring process accessible to all. If you require any reasonable adjustments to fully participate in the application or interview stages, please don’t hesitate to contact your recruiter directly.
We see things differently. Diversity fuels our innovation, we value the unique ways in which we differ, and we are committed to equal employment opportunities for all professionals.
Key Responsibilities
Develop systematic data-transformation modules to gather, clean, validate, and structure raw datasets.
Collaborate closely with Subject Matter Experts (SMEs) and the labeling team, building a solid understanding of domain knowledge and requirements.
Support SMEs and the labeling team by providing tools, solutions, and continuous technical feedback to improve annotation workflows.
Build scalable, reusable data-processing solutions and maintain version control using GitLab.
Troubleshoot and diagnose data issues, proactively flagging inconsistencies or risks.
Maintain a deep understanding of the Data Hub technology stack and data schemas, staying current with emerging technologies, models, and research.
Contribute to broader initiatives related to data integration, feature design, and machine learning.
Design and run experiments, iterate based on results, and document and share learnings with the team.
Use pre-trained ML models, and train or fine-tune models on internal datasets with proper evaluation and validation.
Communicate regularly with team members, the team lead, and cross-functional partners in production and technology to ensure alignment and transparency.
Performance Metrics
Delivers high-quality work on time.
Quickly learns and applies new tools, technologies, and concepts.
Demonstrates strong understanding of Data Hub systems and workflows.
Qualifications & Skills
Background in data science or a related field (Master’s degree preferred).
Proficient in at least one programming language—ideally Python—with experience in common ML libraries.
Experience with hybrid ML workflows (traditional ML, LLMs, embeddings, ontologies, knowledge graphs).
Comfortable working with relational, NoSQL, and graph databases.
Strong data-processing skills, including cleaning, filtering, and feature extraction.
Clear communicator with strong collaboration and presentation skills.
Our Hiring Process
At Viridien, we are committed to delivering a respectful, inclusive, and transparent recruitment experience.
Due to the high volume of applications we receive, we may not be able to provide individual feedback to every applicant. Only candidates whose qualifications closely match the role criteria will be contacted for an interview. We do, however, aim to share personalized feedback with those who progress to the first round of interviews and beyond.
We are also dedicated to ensuring that our hiring process accessible to all. If you require any reasonable adjustments to fully participate in the application or interview stages, please don’t hesitate to contact your recruiter directly.
We see things differently. Diversity fuels our innovation, we value the unique ways in which we differ, and we are committed to equal employment opportunities for all professionals.