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Data Scientist, Product Analytics - Machine Learning

Toronto

We are seeking a talented Data Scientist with a focus on Product Analytics and Machine Learning to join our data forecasting agency. As a Data Scientist in the Product Analytics team, you will be responsible for leveraging advanced analytics techniques and machine learning models to uncover valuable insights from complex data sets. You will collaborate closely with cross-functional teams, including data engineers, software developers, and domain experts, to develop innovative solutions that drive product enhancements and strategic decision-making. The ideal candidate has a strong background in data analysis, statistical modeling, and machine learning, along with a passion for delivering actionable insights through advanced analytics.

Responsibilities:

  • Collaborate with cross-functional teams to define and prioritize analytical problems related to product performance, user behavior, and forecasting.

  • Apply statistical analysis and machine learning techniques to analyze large, complex data sets and derive actionable insights for product optimization.

  • Develop and implement machine learning models, algorithms, and predictive analytics to identify patterns, trends, and anomalies in product data.

  • Conduct exploratory data analysis and feature engineering to uncover valuable insights and improve the accuracy of predictive models.

  • Work closely with data engineers to ensure the availability, quality, and reliability of data used in analytical models and algorithms.

  • Collaborate with software developers to deploy and integrate machine learning models into production systems and enable real-time analytics.

  • Monitor model performance, conduct A/B testing, and provide recommendations for continuous improvement and optimization.

  • Communicate complex analytical findings and insights to stakeholders effectively, both technical and non-technical, through visualizations, reports, and presentations.

  • Stay up to date with the latest advancements in machine learning, data science, and product analytics, and evaluate their potential applications to enhance forecasting capabilities.

  • Mentor and provide guidance to junior data scientists, fostering a collaborative and growth-oriented environment.

Qualifications:

  • Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a related field. A Ph.D. is a plus.

  • Proven experience as a Data Scientist, with a focus on product analytics, machine learning, and predictive modeling.

  • Strong proficiency in statistical analysis, data modeling, and machine learning techniques.

  • Proficiency in programming languages such as Python, R, or SQL, along with experience using machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn).

  • Experience with large-scale data analysis, exploratory data analysis, and feature engineering.

  • Knowledge of statistical methodologies, experimental design, and hypothesis testing.

  • Familiarity with data visualization tools (e.g., Tableau, Power BI) to effectively communicate analytical findings.

  • Strong problem-solving skills and the ability to translate business requirements into analytical solutions.

  • Excellent communication and collaboration skills to work effectively within a multidisciplinary team and interact with stakeholders.

  • Experience with cloud platforms (e.g., AWS, Azure) and distributed computing frameworks (e.g., Apache Spark) is a plus.

  • Understanding of product analytics, user behavior analysis, and forecasting methodologies is highly desirable.

Join our dynamic team and contribute to delivering data-driven insights that shape product strategies and drive business growth. We offer competitive compensation packages and ample opportunities for professional growth and development.

To apply, please submit your resume, along with a cover letter highlighting your relevant experience and explaining why you are interested in this position.

Join us in revolutionizing inventory forecasting with AI

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