Principal Applied Machine Learning Scientist

About Grainger

Grainger is a leading broad line distributor with operations primarily in North America, Japan, and the United Kingdom. We achieve our purpose, We Keep the World Working®, by serving more than 4.5 million customers with a wide range of products that keep their operations running and their people safe. Grainger also delivers services and solutions, such as technical support and inventory management, to save customers time and money.

We're looking for passionate people who can move our company forward. As one of the 100 Best Companies to Work For, we have a welcoming workplace where you can build a career for yourself while fulfilling our purpose to keep the world working. We embrace new ways of thinking and recognize everyone is an individual. Find your way with Grainger today.

Position Details

At Grainger, using insights to better serve our customers has been foundational to our success. We are looking for an Applied Machine Learning Scientists who desires new skills and enjoys applying the latest algorithms and technologies to maintain what has been 95 years of growing success.

Grainger Applied Machine Learning spans across Recommender Systems, Generative AI, Optimization and Simulation, NLP, and other Machine Learning / Deep Learning-based applications. As the Principal applied Machine Learning Scientist you will help transform business processes through data-driven technology products.

Reporting to the Senior Manager of Applied Machine Learning, the Principal Applied Machine Learning Scientist will build scalable machine learning applications by developing and deploying machine learning and deep learning models that improve the effectiveness of our business operations, designing machine learning processes against requirements and automating work across the organization to improve our speed and efficiency. This position will offer a 100% remote work flexibility.

"#LI-Remote"

Compensation

This position is salaried and will pay between $139,740.00 - $214,710.00 and will include an annual cash bonus of 15%.  The range provided is a guideline and not a guarantee of compensation.  Other factors that are involved in offer decisions include, and are not limited to a candidate’s experience, qualifications, geography, and internal equity.

You Will

• Lead the design and implementation of technical solution by applying latest research and AI methods.
• Work with business to understand the problem space, identify the opportunities, and translate business problems into technical solutions using machine learning frameworks.
• Research latest techniques and technologies in the space of ML/AI, Intelligent automation, NLP and evaluate their potential for specific business use cases.
• Build machine learning pipelines from brainstorming, prototyping, development, and deployment.
• Apply techniques such as classification, clustering, regression, NLP, deep learning (CNN, RNN, GANs), time series forecasting, Bayesian methods to build scalable solutions.
• Research and develop techniques in the field of Large Language Models (LLM) and Generative AI.
• Manipulate high-volume, high-dimensionality data from multiple sources, visualize patterns, anomalies, relationships, and trends, and perform feature engineering and selection.
• Create scalable, efficient, automated processes for large scale data analyses, model development, model validation and deployment.
• Collaborate with business, engineering, Machine Learning operations/DevOps and product teams to design and implement AI solutions.

You Have

• PhD and 2 plus years of professional experience, a MS degree and 5 plus years of professional experience or equivalent professional experience.
• Qualifications in a technical field such as Mathematics, Data Science, Applied Analytics, Operations Research, Computer Science, Applied Science or Engineering
• Experience using databases (e.g., Teradata, Snowflake, s3)
• Experience with querying languages (e.g., SQL, SparkSQL)
• Demonstrated advanced programming skills with Python, Pyspark, or C++
• Expertise in deep neural network architectures (CNN, LSTM, Bi-GRUs), attention networks and dynamic memory networks
• Demonstrated knowledge of advanced data science toolsets (e.g., Tensorflow, Pytorch, Scikit-Learn, Airflow, Spark)
• Experience with cloud computing solutions such as AWS, GCP, or Azure
• Experience working in containerization ecosystems (Kubernetes or Docker)
• Experience in consumable endpoints development and deployment (Web Applications, REST APIs)
• Knowledge in model risk management strategies (model registry, concept/covariate drift monitoring, Hyperparameter tuning)
• Experience using advanced computing technologies when needed (e.g. GPUs)
• Followed softwar