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Data Scientist
- Canada
Full-time
This opening has closed.
The original listing is kept below for reference.
Job details
- Job type
- Full-time
- Posting language
- English
- Posted
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Search current openingsOriginal job description
Archived listing. The details below describe a past opening.
Key Responsibilities
- Collect, clean, transform, and analyze structured and unstructured data.
- Identify trends, patterns, and insights from large datasets.
- Develop and validate statistical and machine learning models.
- Perform exploratory data analysis (EDA) and feature engineering.
- Build predictive, classification, clustering, and forecasting models as required.
- Evaluate model performance using appropriate statistical and machine learning metrics.
- Work with stakeholders to understand business problems and translate them into data science solutions.
- Create dashboards, reports, and visualizations to communicate findings effectively.
- Collaborate with data engineers, software developers, product managers, and business teams.
- Deploy and monitor machine learning models in production where applicable.
- Maintain documentation of datasets, models, experiments, and analytical processes.
- Continuously research and evaluate new tools, techniques, and machine learning approaches.
Required Skills
- Strong programming skills in Python or R.
- Good knowledge of statistics and probability.
- Strong understanding of machine learning algorithms and concepts.
- Experience with libraries such as Pandas, NumPy, Scikit-learn, and visualization tools such as Matplotlib or Seaborn.
- Good knowledge of SQL and relational databases.
- Experience with data cleaning, preprocessing, feature engineering, and model evaluation.
- Strong analytical and problem-solving skills.
- Ability to communicate technical findings to both technical and non-technical stakeholders.
Preferred Skills
- Experience with deep learning frameworks such as TensorFlow or PyTorch.
- Knowledge of NLP, computer vision, recommendation systems, or time-series forecasting.
- Experience with cloud platforms such as AWS, Azure, or Google Cloud.
- Knowledge of MLOps, Docker, CI/CD, and model deployment.
- Experience with BI and visualization tools such as Power BI or Tableau.
- Familiarity with big-data technologies such as Spark or Databricks.
More about this job
Responsibilities
Collect, prepare, and analyze structured and unstructured data to identify insights, and develop, validate, evaluate, and where applicable deploy machine learning models. Translate stakeholder needs into data science solutions, communicate findings through reports and visualizations, collaborate across teams, and document analytical work.
Requirements
Requires strong programming skills in Python or R, knowledge of statistics, probability, machine learning, SQL, and relational databases, and experience with data preparation, feature engineering, and model evaluation. Candidates should have strong analytical and problem-solving abilities and be able to communicate technical findings to technical and non-technical stakeholders.
Skills
- Python
- R
- Statistics
- Probability
- Machine Learning
- Pandas
- NumPy
- Scikit-Learn
- Matplotlib
- Seaborn
- SQL
- Data Cleaning
- Feature Engineering
- Model Evaluation
- Data Visualization
- Analytical Problem-Solving
Visa sponsorship
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Categories
- Data & Analytics
- Technology
- Software
- Science & Research
Keywords
- Python
- R
- Statistics
- Probability
- Machine Learning
- Pandas
- NumPy
- Scikit-Learn
- Matplotlib
- Seaborn
- SQL
- Relational Databases
- Exploratory Data Analysis
- Feature Engineering
- Predictive Modeling
- Classification
- Clustering
- Forecasting
- Model Evaluation
- Data Visualization
- TensorFlow
- PyTorch
- Natural Language Processing
- Computer Vision
- Recommendation Systems
- Time-Series Forecasting
- AWS
- Azure
- Google Cloud
- MLOps