Design and implement end-to-end machine learning solutions: analyze use cases, build statistical and ML models, prepare and enrich data, implement scalable PySpark models in Databricks, and collaborate with product and engineering to productionize analytics for personalization and business insights.
Summary:
We are seeking a Machine Learning Engineer to design and develop robust analytics models using statistical and machine learning algorithms. In this role, you will work closely with product and engineering teams to solve complex business problems, identify data-driven opportunities, and create personalized experiences for 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: Analytics Model Development:
- 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.
Data Engineering and Preparation:
- Extend and augment company data with third-party data to enrich analytics capabilities.
- Enhance data collection procedures to include necessary information for building analytics systems.
- Prepare raw data for analysis, including cleaning, imputing missing values, and standardizing data formats using Python data frameworks (e.g., Pandas, NumPy).
Machine Learning Model Implementation:
- 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.
What We’re Looking For:
- Educational Background: Undergraduate or Graduate degree in Computer Science, Mathematics, Physics, or related fields. A PhD is preferred but not necessary.
- Experience:
- 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.
- Technical Skills:
- 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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