Data Scientist

About Fusemachines

Key Responsibilities

• Design, build, and validate time-series forecasting models to predict key business metrics

• Experiment with and evaluate a diverse range of modeling techniques, including traditional ML models (e.g., XGBoost), statistical methods (e.g., Prophet), and advanced deep learning approaches (e.g., DeepAR, N-BEATS).

• Collaborate closely with senior data scientists to analyze business requirements, perform feature engineering, and iterate on model improvements.

• Work with data engineers to understand and utilize complex datasets from Snowflake

• Contribute to the deployment of the model in a cloud environment, ensuring it is scalable and maintainable.

• Take ownership of the long-term model lifecycle, including performance monitoring, retraining, and incorporating new data inputs as they become available.

• Translate complex model outputs into actionable insights and communicate findings to stakeholders across different business units.

Required Skills and Qualifications

• Proven professional experience as a Data Scientist with a strong emphasis on time-series analysis and forecasting.

• Hands-on experience building and deploying forecasting models using libraries/frameworks such as:

  • XGBoost

  • Prophet

  • Deep Learning frameworks (e.g., PyTorch, TensorFlow)

• Proficiency in Python and its core data science ecosystem (e.g., pandas, NumPy, scikit-learn).

• Solid understanding of the underlying statistical and machine learning principles of various forecasting techniques.

• Ability to work collaboratively within a distributed team and effectively partner with client-side data scientists and engineers.

• Strong problem-solving skills with the ability to navigate complex data landscapes and translate business challenges into quantitative models.

Preferred Qualifications

• Direct experience with advanced neural network architectures for time-series forecasting, such as DeepAR or N-BEATS.

• Familiarity with querying and working with data from data warehouses, particularly Snowflake.

• Experience with MLOps practices, including model deployment, monitoring, and maintenance in a cloud environment.

• Domain knowledge in finance, supply chain, or sales operations planning is a significant plus