Senior Data Scientist

Data Scientist

Location: Canada

Job Summary:

Responsibilities: Design and implement AI-powered recommendation systems for various use cases, such as:

• Product recommendations.
• Developing targeted marketing campaigns.
• Demand forecasting and inventory optimization.
• Customer segmentation and lifetime value prediction.

Collect, clean, and preprocess large datasets of customer behavior, product information, and other relevant data.

Train, evaluate, and fine-tune machine learning models using state-of-the-art techniques.

Design AB tests to evaluate models.

Collaborate with engineers to integrate AI models into production systems.

Work closely with product managers and business stakeholders to understand their needs and translate them into technical requirements.

Stay up-to-date with the latest advancements in AI and machine learning for retail applications.

Communicate findings and insights to both technical and non-technical audiences.

Mentor junior data scientists and contribute to the growth of the team.

Qualifications: Master’s or Ph.D. in Computer Science, Machine Learning, Statistics, or a related field, preferred. 6+ years of experience in data science, with a focus on recommendation systems and/or retail AI. Proven experience developing and deploying machine learning models in a production environment. Strong understanding of machine learning algorithms, such as collaborative filtering, content-based filtering, and deep learning-based recommendation models. Proficiency in Python and machine learning frameworks such as scikit-learn, XGBoost, TensorFlow, or PyTorch. Experience with cloud computing platforms such as Azure and Databricks preferred. Experience with data visualization tools such as Tableau or Thoughtspot. Excellent communication and collaboration skills. Strong problem-solving skills and ability to work independently. Preferred Experience: Experience with retail-specific datasets and challenges. Experience with A/B testing and online experimentation. Experience with MLOps and model deployment pipelines. Familiarity with the beauty industry.