Machine Learning Engineer

1 month ago

Dallas, Texas, United States Photon Full time
Job Summary:

We are seeking a highly skilled Machine Learning Engineer to join our team at Photon. As a key member of our analytics team, you will be responsible for designing and developing robust analytics models using statistical and machine learning algorithms.

In this role, you will work closely with our product and engineering teams to solve complex business problems, identify data-driven opportunities, and create personalized experiences for our customers.

You will be responsible for building end-to-end machine learning solutions, implementing models in production, and working with various data frameworks and tools such as Python, Spark, and Databricks.

Key Responsibilities:

  • Analyze use cases and design appropriate analytics models using statistical and machine learning algorithms tailored to specific business requirements.
  • Develop machine learning algorithms to drive personalized customer experiences and provide actionable business insights.
  • Apply expertise in data mining and machine learning techniques, including forecasting, prediction, segmentation, recommendation, and fraud detection.
  • Extend and augment company data with third-party data to enrich analytics capabilities.
  • Prepare raw data for analysis, including cleaning, imputing missing values, and standardizing data formats using Python data frameworks (e.g., Pandas, NumPy).
  • Implement machine learning models, considering both performance and scalability using tools like PySpark in Databricks.
  • Design and build infrastructure to facilitate large-scale data analytics and experimentation.
  • Work with tools like Jupyter Notebooks for data exploration and model development.

Requirements:

  • Undergraduate or Graduate degree in Computer Science, Mathematics, Physics, or related fields.
  • At least 5 years of experience in data analytics, with a strong understanding of core statistical algorithms such as classification and regression analysis.
  • High-level knowledge of analytics use cases such as language analysis, assortment optimization, promotional planning, dynamic pricing, markdown optimization, labor scheduling, and optimization.
  • Strong experience with Python-based machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch).
  • Proficiency in using analytics platforms like Databricks for large-scale data processing.
  • At least 4 years of continuous experience with Spark, particularly PySpark implementation.
  • Hands-on experience with data processing and analysis tools such as Pandas, NumPy, and Jupyter Notebooks.

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