Machine Learning Engineer

Dow Chemical•Houston, TX

10d•Onsite

About The Position

Dow has an exciting and challenging opportunity for a Machine Learning Engineer within our Enterprise Data & Analytics organization located in Houston, TX; Midland, MI; or Champaign, IL. As a Machine Learning Engineer in our Data & Analytics Platforms team specializing in data and analytics solutions, you will work with a cross-functional team whose objective is to deliver solutions that drive business value for Dow. In this role, you will work closely with data engineers, data scientists, domain experts, and software engineers to design, develop, and deploy machine learning systems, including training and inference pipelines on the Azure Databricks platform. You will also help drive a culture around setting standards and adopting best practices for machine learning and MLOps across the organization.

Requirements

A minimum of a Bachelor's degree, or 8 years relevant experience, or relevant military experience at an E6 rank/Petty Officer 2nd Class or higher is required
A minimum of 3 years of experience developing solutions in machine learning, data science, or related field
A minimum requirement for this U.S. based position is the ability to work legally in the United States. No visa sponsorship/support is available for this position, including for any type of U.S. permanent residency (green card) process

Nice To Haves

A degree in computer science, engineering, mathematics, statistics, data science or related field
Proficient in Python and one or more machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, etc
Experience in developing and deploying machine learning models and pipelines on the Databricks platform using Databricks MLflow, Delta Lake, SQL Analytics, Model Registry, Jobs, and Workspace
Strong knowledge of machine learning concepts, techniques, and algorithms
Ability to perform data analysis, feature engineering, model selection, optimization, and evaluation using Databricks
Ability to communicate complex machine learning concepts and results to technical and non-technical audiences using Databricks notebooks and dashboards
Ability to work independently and collaboratively in a fast-paced and dynamic environment
Curiosity and passion for learning new machine learning skills and technologies using Databricks
Strong knowledge of data modeling, data warehousing, and ETL processes
Experience designing and deploying into production both traditional and generative AI systems
Proficiency in SQL and experience with big data technologies such as Apache Spark and Hive
Experience working within Azure Machine Learning
Experience containerizing and deploying ML models to Azure Kubernetes Service
Experience with Azure Data Factory, Azure Data Lake Storage Gen2, and other Azure services
Multi-application and cross-platform design experience
Understanding of data lakehouse platform design and associated workflows
Ability to thrive in challenging situations and solve complex problems
Ability to manage own work effort in multiple projects with little supervision
Interested in emerging technologies with the ability to quickly learn and exploit cutting edge offerings to achieve business objectives

Responsibilities

Designs and implements pipelines and other workflow infrastructure to meet the requirements for new AI/ML solutions that involve online, batch, or real-time inference
Deploys and monitors machine learning models in production using Databricks Model Registry, Jobs, and Workspace
Frequently collaborates with data engineers, DevOps/platform engineers, data scientists, and domain experts as part of a comprehensive MLOps framework to ensure AI/ML solutions are performant, reliable, and maintainable
Works frequently with application development teams to ensure seamless integrations
Works proficiently with various ML frameworks, such as scikit-learn, TensorFlow, PyTorch, Keras, as well as distributed frameworks like Spark MLlib and Ray
Performs data analysis, feature engineering, model selection, hyperparameter optimization, and model evaluation using Databricks MLFlow, Delta Lake, and SQL Analytics and other tools as part of the end-to-end ML lifecycle
Researches and implements new machine learning techniques and methods using Databricks, staying abreast of the latest trends and technologies
Documents and communicates machine learning results and insights to stakeholders using Databricks notebooks and dashboards
Understands IT security policies and implements them as part of new solution designs
Follows and promotes the best practices and standards for machine learning and MLOps across the organization using Databricks and Azure DevOps

Benefits

Equitable and market-competitive base pay and bonus opportunity across our global markets, along with locally relevant incentives.
Benefits and programs to support your physical, mental, financial, and social well-being, to help you get the care you need...when you need it.
Competitive retirement program that may include company-provided benefits, savings opportunities, financial planning, and educational resources to help you achieve your long term financial-goals.
Employee stock purchase programs (availability varies depending on location).
Student Debt Retirement Savings Match Program (U.S. only).
Robust medical and life insurance packages that offer a variety of coverage options to meet your individual needs.
Travel insurance is also available in certain countries/locations.
Opportunities to learn and grow through training and mentoring, work experiences, community involvement and team building.
Workplace culture empowering role-based flexibility to maximize personal productivity and balance personal needs.
Competitive yearly vacation allowance.
Paid time off for new parents (birthing and non-birthing, including adoptive and foster parents).
Paid time off to care for family members who are sick or injured.
Paid time off to support volunteering and Employee Resource Group’s (ERG) participation.
Wellbeing Portal for all Dow employees, our one-stop shop to promote wellbeing, empowering employees to take ownership of their entire wellbeing journey.
On-site fitness facilities to help stay healthy and active (availability varies depending on location).
Employee discounts for online shopping, cinema tickets, gym memberships and more.
Transportation allowance (availability varies depending on location)
Meal subsidiaries/vouchers (availability varies depending on location)
Carbon-neutral transportation incentives e.g. bike to work (availability varies depending on location)