Lead Machine Learning Engineering

About DAT

DAT is an award-winning employer of choice and a next-generation SaaS technology company that has been at the leading edge of innovation in transportation supply chain logistics for 45 years. We continue to transform the industry year over year, by deploying a suite of software solutions to millions of customers every day - customers who depend on DAT for the most relevant data and most accurate insights to help them make smarter business decisions and run their companies more profitably. We operate the largest marketplace of its kind in North America, with 400 million freights posted in 2022, and a database of $150 billion of annual global shipment market transaction data. Our headquarters are in Denver, CO, with additional offices in Missouri, Oregon, and Bangalore, India. For additional information, see www.DAT.com/company .

The Opportunity:

The Data Science team at DAT plays a central role building models, algorithms and tools to deliver insights to our customers, helping them streamline, strengthen, and grow their
businesses. Our team s products run the gamut of data science topics, from time series forecasting, economic modeling, classification, and recommenders. As part of the Data Science team, you will help us move prototypes into production, support all existing ML and statistics products, grow our ML Operations tool set, integrate DS products with the rest of engineering, and engage in research discussions and planning. You will mentor associate MLEs and help uplevel them and the team.

Responsibilities:

Leadership

• The team manager owns the decision of what work is prioritized when, and the ML Engineering Lead takes responsibility for how to get those priorities accomplished.
• Assists the team in breaking large tasks down into smaller, well-groomed actionable tasks. Identify tasks which need more clarity, and places where there are gaps in requirements or capabilities and solve or escalate those issues appropriately
• Communicates regularly with the team manager to stay in lock-step on all team priorities and initiatives
• The lead, along with the team manager, acts as a mentor and coach to other ML engineers as they continually improve their technical and professional skills.
• Involved in most of the team s code reviews and PRs, and takes shared responsibility with the manager in ensuring that all DS products are the highest reasonable quality.
• Make recommendations to the team, and to the manager for any work they deem necessary to maintain a high level of quality, both for new and existing products.
• Stays connected with the broader engineering organization and keeps up to date on company-wide engineering initiatives which affect the DS teams and products.

Engineering

• Supporting the data science family of teams in moving prototype models into production
• Developing sound operations tools for training, release, and rollback of production models
• Developing model monitoring systems to ensure long-term model health and efficacy
• Ownership of production models, ensuring they are properly updated and monitored for efficiency, accuracy, and costs.
• Creating batch processing systems and APIs to deploy and operationalize machine learning models in production environments.
• Working with DAT engineering teams to ensure seamless integrations between DS and the rest of DAT s engineering products.
• Refactoring older data science codebases to bring them up-to-date with current tools and processes
• Supporting data science researchers as a peer, offering both scientific and technical input and feedback

Required Skills/Experience:

• BS or higher degree in Mathematics, Statistics, Physics, Economics, Computer Science or other STEM disciplines
• Four years experience as a data scientist, ML engineer, software engineer with ML experience or similar experience.
• Professional experience building and supporting ML models and processes which are in production and impacting the business
• Professional-level python programming
• Using and/or maintaining CI/CD tools like Jenkins, Codefresh, or similar
• Docker, Kubernetes, or other containerization technologies
• Building or running cloud orchestration systems for data science projects. Such as AWS Sagemaker, Airflow, DataRobot MLOps, or similar.

Professional experience using:

• AWS cloud computing and cloud storage resources for data science project
• Terraform for AWS
• Github and Github Actions

Nice to Have:

• Industry or academic experience in transportation logistics
• Building cloud-based data transformation pipelines (ETL)
• SQL, especially with Snowflake

DAT embraces the value of a diverse workforce, and believes it is a core strength of our company that we encourage those values in every DAT employee, at every level of our organization, regardless of tenure or rank. We provide equal employment opportunities (EEO) to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, disability, genetic information, marital status, amnesty, or status as a covered veteran in accordance with applicable federal, state, and local laws.

Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities

The contractor will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor s legal duty to furnish information. 41 CFR 60-1.35(c)

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