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Applied Machine Learning Scientist

Toronto

We are seeking an experienced Applied Machine Learning Scientist to join us. As an Applied Machine Learning Scientist, you will play a critical role in developing and applying advanced machine learning techniques to solve complex forecasting problems for our clients. Working closely with our data scientists, domain experts, and engineers, you will apply your expertise in machine learning, statistics, and data analysis to build robust models that provide accurate predictions and insights. The ideal candidate is passionate about leveraging cutting-edge technologies to unlock the potential of data and drive strategic decision-making.

Responsibilities:

  • Collaborate with cross-functional teams to understand business requirements and design machine learning solutions for data forecasting challenges.

  • Develop and implement state-of-the-art machine learning models, algorithms, and techniques to extract insights and predict trends from diverse data sets.

  • Conduct rigorous data exploration, preprocessing, and feature engineering to optimize model performance and accuracy.

  • Apply statistical methodologies and experimental design to evaluate and validate the performance of machine learning models.

  • Employ advanced techniques in model training, optimization, and validation to improve forecasting accuracy and robustness.

  • Collaborate with software engineers to deploy machine learning models into production systems, ensuring scalability and performance.

  • Explore and incorporate emerging machine learning techniques and technologies to enhance forecasting capabilities and stay at the forefront of the field.

  • Conduct thorough analysis of model outputs and provide clear interpretations and actionable insights to both technical and non-technical stakeholders.

  • Collaborate with clients to understand their specific forecasting needs and develop tailored solutions to address their unique challenges.

  • Stay up to date with the latest research in machine learning, data science, and related fields to inform and advance our forecasting capabilities.

  • Mentor and provide guidance to junior team members, sharing knowledge and best practices.

Qualifications:

  • Ph.D. or Master's degree in Computer Science, Statistics, Mathematics, or a related field with a focus on machine learning.

  • Proven experience as an Applied Machine Learning Scientist or similar role, with a track record of developing and implementing machine learning models for complex forecasting problems.

  • Strong expertise in machine learning techniques, such as regression, classification, time series forecasting, and deep learning.

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

  • Solid understanding of statistical methodologies, experimental design, and hypothesis testing.

  • Experience with data preprocessing, feature engineering, and exploratory data analysis.

  • Familiarity with distributed computing frameworks (e.g., Apache Spark) and cloud platforms (e.g., AWS, Azure) for handling large-scale data processing is desirable.

  • Strong analytical and problem-solving skills, with the ability to translate complex business requirements into effective machine learning solutions.

  • Excellent communication and presentation skills, with the ability to convey technical concepts to both technical and non-technical stakeholders.

  • Proven ability to work in a collaborative team environment, driving projects from conception to completion.

We offer competitive compensation packages and a stimulating work environment that encourages innovation and professional growth. Join our team and contribute to solving challenging forecasting problems using cutting-edge machine learning techniques.

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

La prévision des stocks est un défi multisectoriel

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