Burtch Works
Applied ML Scientist – Product Discovery
Burtch Works, Chicago, Illinois, United States, 60290
Overview
Job Title: Applied ML Scientist. Location: Hybrid in Chicago. About The Company
We are a leading broad line distributor with operations primarily in North America, Japan and the United Kingdom. Our mission is to keep the world working by serving more than 4.5 million customers worldwide with products and solutions delivered through innovative technology and deep customer relationships. We are committed to service excellence, fostering an award-winning culture, and maintaining our position as an industry leader with $17.2 billion in annual revenue across our business segments. Job Summary
We are looking for an Applied ML Scientist to join our Product Discovery team in Chicago (hybrid). The ideal candidate will be a motivated individual with strong technical skills in machine learning and deep learning, eager to develop cutting-edge search and recommendation solutions. This role will involve leveraging advanced technologies such as Product Graphs, Deep Learning, Graph Neural Networks (GNNs), Large Language Models (LLMs), and Embeddings to streamline operational efficiency and facilitate seamless customer experiences. Responsibilities
Model Development: Contribute to the design and development of machine learning models supporting Search, Recommendations, and Product Associations, under the guidance of senior team members, focusing on delivering measurable improvements to customer discovery experiences. Deep Learning Implementation: Implement and experiment with deep learning and embedding-based techniques, including DSSM or transformer-based architectures, in collaboration with technical leads to enhance product discovery capabilities. Product Association Modeling: Support the development of product association models (e.g., co-viewed, substitutes, complements) using methods such as collaborative filtering, graph features, or rule-based approaches to improve cross-selling and upselling opportunities. Advanced ML Technologies: Work with GNNs, LLMs, and product embeddings to improve personalization and relevance across product discovery platforms, ensuring customers find the most suitable products for their needs. Production Deployment: Contribute to model deployment efforts using Kubernetes and cloud-native tools, learning to optimize inference pipelines in production environments for scalable and reliable service delivery. End-to-End ML Workflow: Participate in the complete ML workflow including data exploration (via Snowflake/SQL), model evaluation, monitoring, and basic A/B testing to ensure robust and effective solutions. Requirements
Education: Advanced degree (PhD or Master’s) in Applied Mathematics, Physics, Engineering, Computer Science, Electrical Engineering, or related field OR Bachelor’s degree with 2+ years of relevant experience OR equivalent practical experience Experience: Strong foundation in machine learning with hands-on experience in model development and deployment Skills: Strong coding skills in Python with exposure to deep learning frameworks like PyTorch, TensorFlow, or Keras; proficiency in SQL and large-scale data processing using tools like PySpark; experience with large-scale data platforms (e.g., Snowflake, Hive) Technical Knowledge: Exposure to state-of-the-art deep learning techniques (e.g., transformer-based models) and/or causal inference modeling Deployment Experience: Basic understanding of deploying models with Docker, APIs, or orchestration tools like Airflow Preferred Qualifications
Exposure to graph-based machine learning (e.g., basic GNNs, product graphs) or recommender systems Working knowledge of LLMs, embeddings, or NLP techniques for semantic search or ranking improvements Experience working in cloud environments (e.g., GCP, AWS, Azure) Demonstrated curiosity and eagerness to learn advanced topics in scalable ML, model monitoring, or real-time inference Benefits
Competitive Salary: $150k – $180k base compensation range Health and Wellness: Medical, dental, vision, and life insurance plans with coverage starting on day one of employment and 6 free sessions each year with a licensed therapist to support your emotional wellbeing Work-Life Balance: 18 paid time off (PTO) days annually for full-time employees (accrual prorated based on employment start date) and 6 company holidays per year Professional Development: Tuition reimbursement, student loan refinancing, and free access to financial counseling, education, and tools Additional Perks: 6% company contribution to a 401(k) Retirement Savings Plan each pay period with no employee contribution required, employee discounts, maternity support programs, nursing benefits, and up to 14 weeks paid leave for birth parents and up to 4 weeks paid leave for non-birth parents
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Job Title: Applied ML Scientist. Location: Hybrid in Chicago. About The Company
We are a leading broad line distributor with operations primarily in North America, Japan and the United Kingdom. Our mission is to keep the world working by serving more than 4.5 million customers worldwide with products and solutions delivered through innovative technology and deep customer relationships. We are committed to service excellence, fostering an award-winning culture, and maintaining our position as an industry leader with $17.2 billion in annual revenue across our business segments. Job Summary
We are looking for an Applied ML Scientist to join our Product Discovery team in Chicago (hybrid). The ideal candidate will be a motivated individual with strong technical skills in machine learning and deep learning, eager to develop cutting-edge search and recommendation solutions. This role will involve leveraging advanced technologies such as Product Graphs, Deep Learning, Graph Neural Networks (GNNs), Large Language Models (LLMs), and Embeddings to streamline operational efficiency and facilitate seamless customer experiences. Responsibilities
Model Development: Contribute to the design and development of machine learning models supporting Search, Recommendations, and Product Associations, under the guidance of senior team members, focusing on delivering measurable improvements to customer discovery experiences. Deep Learning Implementation: Implement and experiment with deep learning and embedding-based techniques, including DSSM or transformer-based architectures, in collaboration with technical leads to enhance product discovery capabilities. Product Association Modeling: Support the development of product association models (e.g., co-viewed, substitutes, complements) using methods such as collaborative filtering, graph features, or rule-based approaches to improve cross-selling and upselling opportunities. Advanced ML Technologies: Work with GNNs, LLMs, and product embeddings to improve personalization and relevance across product discovery platforms, ensuring customers find the most suitable products for their needs. Production Deployment: Contribute to model deployment efforts using Kubernetes and cloud-native tools, learning to optimize inference pipelines in production environments for scalable and reliable service delivery. End-to-End ML Workflow: Participate in the complete ML workflow including data exploration (via Snowflake/SQL), model evaluation, monitoring, and basic A/B testing to ensure robust and effective solutions. Requirements
Education: Advanced degree (PhD or Master’s) in Applied Mathematics, Physics, Engineering, Computer Science, Electrical Engineering, or related field OR Bachelor’s degree with 2+ years of relevant experience OR equivalent practical experience Experience: Strong foundation in machine learning with hands-on experience in model development and deployment Skills: Strong coding skills in Python with exposure to deep learning frameworks like PyTorch, TensorFlow, or Keras; proficiency in SQL and large-scale data processing using tools like PySpark; experience with large-scale data platforms (e.g., Snowflake, Hive) Technical Knowledge: Exposure to state-of-the-art deep learning techniques (e.g., transformer-based models) and/or causal inference modeling Deployment Experience: Basic understanding of deploying models with Docker, APIs, or orchestration tools like Airflow Preferred Qualifications
Exposure to graph-based machine learning (e.g., basic GNNs, product graphs) or recommender systems Working knowledge of LLMs, embeddings, or NLP techniques for semantic search or ranking improvements Experience working in cloud environments (e.g., GCP, AWS, Azure) Demonstrated curiosity and eagerness to learn advanced topics in scalable ML, model monitoring, or real-time inference Benefits
Competitive Salary: $150k – $180k base compensation range Health and Wellness: Medical, dental, vision, and life insurance plans with coverage starting on day one of employment and 6 free sessions each year with a licensed therapist to support your emotional wellbeing Work-Life Balance: 18 paid time off (PTO) days annually for full-time employees (accrual prorated based on employment start date) and 6 company holidays per year Professional Development: Tuition reimbursement, student loan refinancing, and free access to financial counseling, education, and tools Additional Perks: 6% company contribution to a 401(k) Retirement Savings Plan each pay period with no employee contribution required, employee discounts, maternity support programs, nursing benefits, and up to 14 weeks paid leave for birth parents and up to 4 weeks paid leave for non-birth parents
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