Job Location: Vancouver, British Columbia, Canada
Job Category: Data Science / Machine Learning / AI & Analytics
Experience: Entry Level to Senior Level
Employment Type: Full-Time / Permanent / Contract – Depending on Opening
Work Model: On-Site / Hybrid / Remote – Depending on Employer
Salary: Open for the Right Candidates
Company: Cybotrix Technologies
Data Science Jobs in Vancouver
Explore Data Science Jobs in Vancouver with Cybotrix Technologies for entry-level, mid-level and senior professionals interested in building careers across data science, machine learning, artificial intelligence and advanced analytics.
Vancouver and the wider Metro Vancouver region support a diverse technology ecosystem covering software, SaaS, artificial intelligence, fintech, life sciences, digital media and other technology-driven industries. As a result, Data Science professionals can explore opportunities where data is used to improve products, automate decisions, understand customers and develop intelligent applications.
Candidates with knowledge of Python, R, SQL, machine learning, statistics, Scikit-learn, TensorFlow, PyTorch, NLP, Generative AI, AWS, Azure and Google Cloud can explore suitable positions based on their experience and specialization.
Data Science Roles in Vancouver
Data Science offers multiple career paths. For example, graduates may begin with junior analytical or Data Science positions, while experienced professionals can progress towards advanced machine learning and leadership roles.
Potential opportunities may include:
Junior Data Scientist, Data Scientist, Applied Data Scientist, Machine Learning Scientist, Product Data Scientist, AI Data Scientist, NLP Data Scientist, Statistical Analyst, Decision Scientist, Predictive Analytics Specialist, Senior Data Scientist, Lead Data Scientist and Principal Data Scientist.
However, job titles and responsibilities can differ considerably between employers. Therefore, candidates should evaluate the technical requirements of each position before applying.
Key Responsibilities
Depending on the role, Data Scientists may:
- Collect and prepare datasets for analysis.
- Perform exploratory data analysis.
- Develop statistical and predictive models.
- Build machine learning solutions.
- Conduct feature engineering.
- Train and evaluate models.
- Write Python, R and SQL code.
- Identify patterns and trends in complex datasets.
- Develop data visualizations.
- Conduct experiments and model validation.
- Work with structured and unstructured data.
- Support AI and machine learning applications.
- Present analytical findings to stakeholders.
- Collaborate with Data Engineers and Software Engineers.
- Document models, assumptions and analytical methodologies.
In addition, experienced Data Scientists may define modelling strategies, review technical approaches and mentor junior professionals.
Python Data Science Jobs
Python is an important programming language across Data Science and machine learning environments.
Relevant technologies can include Python, Pandas, NumPy, SciPy, Scikit-learn, Matplotlib, TensorFlow, PyTorch and Jupyter-based environments.
For example, Data Scientists may use Python to clean datasets, perform statistical analysis, develop machine learning models and automate analytical workflows.
Moreover, strong programming fundamentals can help candidates move beyond experimentation and contribute to production-oriented Data Science projects.
SQL & Data Analysis Skills
Data Scientists often need to retrieve and analyse information stored across databases and analytical platforms. Therefore, SQL remains an important skill.
Relevant knowledge may include joins, CTEs, aggregations, subqueries, window functions, data transformation and query optimisation.
Furthermore, an understanding of database structures can make collaboration with Data Engineers and analytics teams more effective.
Machine Learning Jobs in Vancouver
Machine learning is a major specialization within modern Data Science.
Depending on the application, professionals may work with:
Regression, Classification, Clustering, Forecasting, Recommendation Systems, Anomaly Detection, Ranking Models and other predictive techniques.
Relevant frameworks may include Scikit-learn, XGBoost, LightGBM, TensorFlow and PyTorch.
However, building an accurate model is only one part of the process. Candidates should also understand data preparation, feature engineering, validation, evaluation metrics and model limitations.
AI & Applied Data Science Roles
Vancouver’s broader technology ecosystem includes significant activity in artificial intelligence and applied AI. Consequently, Data Scientists with AI-focused skills may explore opportunities involving intelligent products and advanced analytical applications.
Relevant areas can include Natural Language Processing, Computer Vision, Generative AI, Large Language Models, Recommendation Systems and predictive analytics.
Depending on the organization, Data Scientists may work alongside Machine Learning Engineers, Software Developers, Data Engineers and product teams.
Generative AI & LLM Skills
Some modern Data Science positions may involve Generative AI technologies.
Relevant knowledge can include:
Large Language Models, Prompt Engineering, Embeddings, Vector Databases, Retrieval-Augmented Generation (RAG), Semantic Search and model evaluation.
For instance, these technologies can support document intelligence, search, knowledge assistants, content analysis and conversational applications.
At the same time, candidates should understand privacy, data quality, model evaluation and responsible AI practices.
NLP Data Scientist Jobs
Natural Language Processing professionals develop solutions that analyse and understand text or language-based information.
Relevant skills may include NLP, Transformers, Text Classification, Named Entity Recognition, Sentiment Analysis, Embeddings, Semantic Search and Large Language Models.
In addition, strong Python and machine learning fundamentals can help candidates work effectively on NLP-focused projects.
Data Science in Vancouver’s Life Sciences Sector
Metro Vancouver also has a substantial life sciences ecosystem, and British Columbia’s life sciences sector is increasingly connected with AI and computational technologies.
As a result, some Data Science professionals may find opportunities involving health data, computational research, biotechnology, drug discovery, predictive modelling and other data-intensive life sciences applications.
However, requirements can differ significantly from general technology positions. Candidates targeting scientific Data Science roles may benefit from relevant domain knowledge alongside programming and statistical skills.
Statistical Analysis & Experimentation
Strong statistical knowledge helps Data Scientists evaluate evidence and uncertainty rather than relying only on algorithms.
Important concepts can include:
Probability, Statistical Distributions, Regression, Hypothesis Testing, Experimental Design, A/B Testing, Time-Series Analysis and model evaluation.
For example, product-focused Data Scientists may use experiments to understand whether a product change improves user behaviour or business outcomes.
Therefore, candidates should be able to explain both the methodology and the practical meaning of analytical results.
Product Data Science Jobs
Vancouver’s software and technology environment can also create opportunities for product-oriented Data Scientists.
Product Data Scientists may analyse:
Customer Behaviour, User Engagement, Retention, Conversion, Product Funnels, Experiments and feature performance.
Unlike purely research-oriented roles, these positions often require close collaboration with product managers, engineers and business stakeholders.
Consequently, strong communication and business understanding can be just as important as technical ability.
Cloud Data Science Skills
Many Data Science workflows now operate on cloud infrastructure.
Depending on the employer, candidates may work with AWS, Microsoft Azure or Google Cloud Platform.
Cloud experience can involve storage, notebooks, compute resources, machine learning services and model deployment environments.
Furthermore, familiarity with cloud-based data platforms can make it easier to collaborate with Data Engineering and MLOps teams.
Entry-Level Data Science Jobs in Vancouver
Candidates beginning their careers can explore opportunities such as Junior Data Scientist, Data Science Associate, Junior Analytics Specialist and entry-level Applied Data Science roles.
Initially, candidates should develop strong foundations in:
Python, SQL, Statistics, Data Analysis, Machine Learning Fundamentals and Data Visualization.
Knowledge of R may also be valuable for statistically intensive positions.
In addition, graduates can strengthen their profiles with internships, academic research, GitHub projects or a practical Data Science portfolio.
For example, a portfolio could include projects involving forecasting, recommendation systems, classification, NLP, customer analytics or predictive modelling.
More importantly, each project should explain the problem, dataset, methodology, evaluation process and final findings rather than displaying code alone.
Mid-Level Data Scientist Roles
After gaining practical experience, Data Scientists can take greater ownership of analytical and machine learning projects.
At this level, professionals may:
- Develop production-oriented machine learning models.
- Perform advanced statistical analysis.
- Conduct feature engineering.
- Design experiments.
- Evaluate model performance.
- Build reusable analytical workflows.
- Collaborate with Data Engineering teams.
- Communicate recommendations to stakeholders.
- Support deployment and monitoring.
- Improve existing analytical solutions.
Furthermore, professionals may specialize in Machine Learning, Product Data Science, NLP, Applied AI, Forecasting or Decision Science.
Senior Data Scientist Jobs in Vancouver
Senior Data Scientists may lead complex analytical initiatives and help establish technical direction.
For instance, senior professionals may design modelling approaches, evaluate analytical methodologies and guide teams through difficult Data Science problems.
Responsibilities can include:
- Leading Data Science projects.
- Designing advanced predictive models.
- Defining model evaluation standards.
- Reviewing statistical methodologies.
- Working with large and complex datasets.
- Supporting production AI systems.
- Mentoring junior Data Scientists.
- Collaborating with engineering and product teams.
- Presenting recommendations to stakeholders.
- Contributing to AI and Data Science strategy.
As professionals gain experience, career opportunities may include Senior Data Scientist, Lead Data Scientist, Principal Data Scientist, Applied AI Lead and Data Science Manager.
Important Data Science Skills
Employers may look for different combinations of technical and analytical capabilities.
Programming: Python, R
Data Querying: SQL
Data Analysis: Pandas, NumPy
Machine Learning: Scikit-learn, XGBoost, LightGBM
Deep Learning: TensorFlow, PyTorch
Statistics: Probability, regression, experimentation
AI: NLP, LLMs, RAG, embeddings
Visualization: Power BI, Tableau, Matplotlib
Cloud: AWS, Azure, GCP
Big Data: Spark, PySpark
Development: Git and version control
Communication: Explaining analytical findings clearly
However, candidates are not expected to know every technology listed. The required skill set depends on the specific Data Science position.
Data Scientist vs Data Analyst
Although both professionals work with data, their core responsibilities can differ.
A Data Analyst generally concentrates on SQL, dashboards, reporting, KPIs, visualization and business analysis.
In comparison, a Data Scientist usually works more extensively with statistics, predictive modelling, machine learning and experimentation.
Nevertheless, responsibilities can overlap in some organizations. Candidates should therefore evaluate the actual job description rather than relying only on the title.
Data Scientist vs Data Engineer
Data Scientists and Data Engineers also perform different but complementary functions.
A Data Engineer builds pipelines and platforms that collect, transform and deliver reliable data. A Data Scientist, on the other hand, uses available information to perform analysis, develop predictive models and create machine learning solutions.
As a result, the two roles frequently collaborate on AI, analytics and data-driven product development.
Data Science Career Growth
Data Science offers several technical and leadership pathways.
One potential progression is:
Junior Data Scientist → Data Scientist → Senior Data Scientist → Lead Data Scientist → Principal Data Scientist
Alternatively, professionals can specialize in Machine Learning, Applied AI, Generative AI, NLP, Product Data Science, Decision Science, Forecasting or advanced analytics.
Professionals interested in management may eventually progress towards Data Science Manager, Director of Data Science or other data and AI leadership roles.
Data Science Opportunities in Vancouver
Vancouver and the broader Metro Vancouver region provide a technology environment spanning software and IT, SaaS, AI, fintech, life sciences and other innovation-focused industries. Invest Vancouver reports a substantial regional software and IT workforce and identifies applied AI as an important part of the area’s technology ecosystem.
Depending on the employer, Data Science positions may be on-site, hybrid or remote. However, workplace arrangements, Canadian work authorization requirements and location restrictions should be checked for every individual vacancy.
Candidates should also review whether a role requires specific industry experience, particularly for positions in regulated sectors, healthcare, financial services or sensitive data environments.
Apply for Data Science Jobs in Vancouver
Looking for Data Science Jobs in Vancouver? Cybotrix Technologies welcomes applications from eligible entry-level, mid-level and senior professionals seeking suitable opportunities in Vancouver and the surrounding region.
Applicants can highlight experience with Python, R, SQL, machine learning, statistics, TensorFlow, PyTorch, Scikit-learn, NLP, Generative AI, Power BI, Tableau, AWS, Azure and GCP. In addition, relevant certifications, internships, research and practical projects can strengthen a candidate’s profile.
Whether you are starting a Data Science career or progressing towards a senior position, explore suitable opportunities ranging from Junior Data Scientist and Data Scientist to Senior Data Scientist, Lead Data Scientist and specialized AI roles with Cybotrix Technologies.