Data Scientist ML
New
We are currently looking for a Data Scientist_ML in Canada.Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- Minimum of 5 years
- Required Skills
- PythonSQLCloud ComputingMachine LearningData scienceTensorflowMLOpsPySpark
Requirements
- Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
- Minimum of 5 years of hands-on experience in Data Science and Machine Learning roles.
- Strong expertise in mathematics, probability, statistics, regression analysis, and traditional machine learning algorithms.
- Advanced programming skills in Python, including experience with Pandas, NumPy, Scikit-learn, TensorFlow, and object-oriented programming concepts.
- Experience working with PySpark and large-scale distributed data processing systems.
- Strong SQL skills and experience with relational databases, data warehouses, and large-scale data environments.
- Hands-on experience building APIs and ML services using frameworks such as Flask or similar technologies.
- Proven experience in Retail, Consumer Packaged Goods (CPG), or E-Commerce domains.
- Familiarity with cloud-based ML and MLOps platforms such as Databricks, Vertex AI, Amazon SageMaker, Azure Machine Learning, or similar solutions.
- Experience building and maintaining CI/CD pipelines for ML workflows and production deployment.
- Strong communication, problem-solving, collaboration, and mentoring skills in cross-functional environments.
Responsibilities
- Design, develop, and deploy scalable machine learning solutions focused on pricing optimization, demand forecasting, promotion planning, and revenue forecasting.
- Build and enhance statistical and ML models for demand prediction, price elasticity analysis, inventory optimization, and retail performance forecasting.
- Develop and maintain robust ML pipelines, production-ready APIs, and model monitoring systems within modern cloud and MLOps environments.
- Analyze large-scale structured and unstructured datasets to uncover trends, customer behavior insights, and opportunities for pricing and revenue optimization.
- Evaluate and implement advanced forecasting techniques, including transformer-based time series models for large-scale retail demand planning.
- Drive experimentation, model evaluation, validation, and continuous optimization of machine learning workflows and predictive systems.
- Collaborate cross-functionally with Product, Engineering, Data Engineering, and business stakeholders to translate business challenges into scalable AI solutions.
- Mentor junior data scientists, provide technical leadership, and communicate analytical insights effectively to both technical and non-technical audiences.
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