Senior Data Scientist

Global Spatial Technology Solutions; Montreal; Remote; CAD 85,000 - 110,000; Full time

Job summary

A leading AI firm based in Canada is seeking a Data Scientist to lead the development of AI solutions for the maritime industry. The ideal candidate will have over 5 years of experience in machine learning and data science, with a strong ability to work with geospatial data. This position offers a remote-first culture and opportunities for professional growth and development.

Benefits

Flexible working conditions

Comprehensive health coverage

Continuous learning opportunities

Work-life balance

Qualifications

• 5+ years of experience developing machine learning models.
• Expertise in geospatial and time-series data handling.
• Excellent communication skills.

Responsibilities

• Lead design and deployment of AI/ML models using various data.
• Mentor junior data scientists and ensure best practices.
• Present findings to stakeholders.

Skills

Machine learning model development

Data analysis

Python programming

SQL proficiency

Team mentoring

Education

Bachelor’s degree in Engineering, Physics, Geomatics, Data Science

Tools

Python libraries (pandas, NumPy, etc.)

AWS

Relational databases

Job description

WHAT WE DO

Global Spatial Technology Solutions (GSTS) is a fast growing, leading-edge Artificial Intelligence company developing solutions for the global maritime sector, our solutions process large volumes of satellite and terrestrial data in real-time to provide decision-making intelligence to save lives, energy and the environment.

We are expanding our team to support the global roll-out of this new capability that will have a major impact on maritime risk and compliance management. We seek individuals with drive, initiative and motivation to join our team in making the world a safer, cleaner place for all.

THE ROLE

This position will lead the development of advanced AI and data science solutions to transform decision making in the maritime domain. You will play a critical role in shaping data strategies, mentoring team members, and delivering high-impact models that enhance safety, efficiency, and sustainability. This position is ideal for individuals who excel in applying technical innovation to real-world challenges and thrive in collaborative, mission-driven environments.

KEY RESPONSIBILITIES

• Lead the design, development, and deployment of AI/ML models using maritime, geospatial, and environmental data.
• Drive exploratory data analysis and prototyping to discover insights and guide product direction.
• Mentor and support junior data scientists in technical execution and best practices.
• Architect data workflows and contribute to scalable data infrastructure in collaboration with engineering teams.
• Translate business and stakeholder needs into robust analytical solutions and model-driven insights.
• Conduct rigorous testing, validation, and performance tuning of models.
• Present findings and strategic recommendations to cross-functional teams and external stakeholders.
• Stay current on advances in AI and data science to inform technology adoption and roadmap planning.

EXPERIENCE & QUALIFICATIONS

• Bachelor’s degree in Engineering, Physics, Geomatics, Data Science, or a related discipline.
• 5+ years of experience in developing and implementing machine learning models and data science solutions.
• Expertise in handling geospatial, graph, and time-series data.
• Proficiency in Python, including core data science and geospatial libraries (e.g., pandas, NumPy, Polars, scikit-learn, PyTorch, geopandas, shapely).
• Strong SQL and experience working with relational databases.
• Demonstrated experience deploying models to production, ideally within cloud environments (AWS preferred).
• Strong background in probability theory, statistical methods, and a keen sense for causal mechanisms behind non-linear model outcomes.
• Proven ability to lead data science initiatives and cross-functional projects.
• Excellent communication skills and the ability to convey technical content to non-technical stakeholders.
• Strong attention to detail and a commitment to clear, reproducible documentation.

NICE TO HAVE

• Maritime or ocean technology sector background.
• Familiarity with DevOps practices and containerization tools.
• Experience in model monitoring and MLOps best practices.

WHY WORK WITH US?

• Remote-first culture:Enjoy the flexibility of working from anywhere in Canada, allowing you to balance your professional and personal life. Prefer to be in office? We also have Dartmouth, Nova Scotia and Montreal, Quebec offices offering flexible hybrid options.
• Have a meaningful impact:Join a dynamic environment where your contributions directly influence our products and company growth.
• Collaborative team:Be part of a small, agile team that values positivity, creativity, and open communication.
• Comprehensive health coverage:We offer competitive health benefits for you and your family, including a wellness incentive to support your overall well-being.
• Continuous learning:We’re committed to your professional growth, providing opportunities for skill development and staying current with industry trends.
• Exciting industry:Work on cutting-edge projects in a rapidly evolving field, applying your skills to solve real-world challenges.
• Work-life balance:We respect your time outside of work and strive to maintain a healthy balance between professional and personal commitments.

We are committed to creating a sense of belonging amongst our team. We have placed an emphasis on fostering a diverse, collaborative, and inclusive working environment. We welcome applications from qualified candidates irrespective of social and cultural background, age, gender, disability, sexual orientation or religious belief.

GSTS offers a competitive salary and benefits package, if you think this challenge is right for you, we encourage you to apply. Please address any questions to [e-mail].

Comments/Special Considerations

Candidates must be willing to obtain a criminal background check and provide two professional references.

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