Manager, Data Science & Machine Learning Engineering

Description

Supervise the day-to-day functions of the Machine Learning (ML) team to create and execute a strategy for the company’s ML technology to analyze millions of phone calls that stream through our proprietary platform every day, ensuring the delivery of data science and machine learning projects with significant business impact and using fundamental computer science concepts, software design best practices, software development life cycle, and machine learning design patterns. Duties include:

1. Collaborating closely with business consulting staff and leaders in multi-disciplinary teams to manage the development of data-driven solutions for clients across diverse sectors.

2. Translating business objectives into data and analytics solutions, leveraging the latest data engineering, machine learning, and advanced analytics applications.

3. Partnering with engineering and product specialists to lead the development of innovative analytics solutions, including those employing natural language processing (NLP) techniques and other machine learning concepts, techniques, and algorithms, including large language models, transformer models, deep learning, and other advanced technologies.

4. Leading the development and deployment of highly scalable, production-grade machine learning solutions and software, integrating the latest advancements in ML and analytics.

5. Fostering the creation of re-usable frameworks, models, and components, staying at the forefront of emerging technologies.

6. Championing best practices in ML engineering, MLOps, and the utilization of advanced analytics to ensure reliable deployment of innovative solutions.

7. Cultivating relationships with external data and analytics vendors to stay updated on industry advancements, including the latest technologies.

8. Providing thought leadership in ML techniques, with a focus on their practical application in conversational analytics and related fields.

9. Managing the deployment of industry-leading machine learning solutions to address client challenges across industry verticals, particularly in the realm of conversational analytics.

10. Mentoring Machine Learning Engineers and offering expertise to ensure projects are completed on time and to client specification.

11. Collaborating and communicating with other company leadership, colleagues, clients, and other stakeholders at all levels to deliver highest quality solutions and expand machine learning, engineering, and analytics capabilities, with a focus on staying at the forefront of conversational analytics trends and technologies.

12. Contributing to the development of advanced analytics intellectual property, leveraging the latest technologies and identifying new opportunities for data science and analytics, especially in the field of conversational analytics.

13. Working independently and as part of a team, adapting to fast-paced, ambiguous, and complex environments and situations.

14. Utilizing expert-level knowledge of fundamental computer science concepts, software design best practices, software development life cycle, and machine learning design patterns; machine learning concepts, techniques, and algorithms, including large language models, deep learning,

and natural language processing; linear algebra techniques and optimization algorithms relevant for machine learning model implementation; Python programming as well as machine learning frameworks including Sci-kit Learn, TensorFlow, Keras, and PyTorch; cloud platform (AWS, GCP, Azure) and associated machine learning services; big data technologies including HDFS, Spark, and Kafka; and Agile software development practices.

This is a telecommuting position and the employee may live anywhere in northeast Texas that allows for commuting to the company office in Dallas, TX as needed to attend company meetings and visit client sites.

Requirements

 Position requires (1) Master of Science in Computer Science, Information Technology, or closely-related field. (2) 24 months of experience as a Machine Learning Engineer, including each of the following: (a) Using fundamental computer science concepts, software design best practices, software development life cycle, and machine learning design patterns; (b) Using machine learning concepts, techniques, and algorithms, including large language models, transformer models, deep learning, and natural language processing; (c) Deploying production-grade machine learning solutions; (d) Using Python programming as well as machine learning frameworks including Sci-kit Learn, TensorFlow, Keras, and PyTorch; (e) Using at least one cloud platform (AWS, GCP, or Azure) and associated machine learning services. (f) Using big data technologies including HDFS, Spark, and Kafka; (g) Using Agile software development practices; (h) Communicating with colleagues, clients, and stakeholders at all levels; (i) Delivering data science and machine learning projects with significant business impact; and (j) Working independently and as part of a team, adapting to fast-paced, ambiguous, and complex environments and situations. (3) Successful completion of drug screen and background check.