Description

You will build and maintain machine learning solutions from problem definition through production deployment.

Responsibilities

• Translate product and business problems into data-driven ML solutions.
• Develop, validate, and iterate on predictive models using supervised, unsupervised, or time series techniques.
• Build and maintain pipelines for model training, evaluation, and inference.
• Deploy ML models into production, ensuring scalability and observability.
• Monitor model drift and performance, developing retraining and versioning strategies.

Required Skills

• 5+ years of experience in data science and machine learning, with production delivery experience.
• Proficiency in Python and ML libraries (scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow).
• Extensive experience with univariate and multivariate time series forecasting models (LGBM, Prophet).
• Experience deploying models via APIs (Flask, FastAPI), Docker, and orchestration tools (Airflow, Kubeflow, MLflow).
• Solid experience with SQL and data manipulation tools (Pandas, Spark).
• Hands-on experience with cloud platforms (AWS, GCP, Azure).
• Strong understanding of statistical modeling, ML algorithms, and experiment design.
• Experience implementing MLOps practices.

Key Skills

Education

Bachelor's degree