Direct-hire Director Data Science Opportunity in Irving, TX

Job Summary

Our Client’s Machine Learning Engineering group is looking for a Director of Data Science to lead our machine learning productization efforts.   The Machine Learning Engineering team is responsible for integrating machine learning capabilities into our client’s PeopleCloud suite of marketing SaaS, focusing on the client’s People Cloud Loyalty, Messaging and Customer product solutions.  You will be part of the Machine Learning Engineering leadership team which manages a group comprised of data scientist and software developers that are researching and building automated, repeatable segmentation, predictive modeling and recommendation solutions that directly impact the marketing performance of our clients leveraging the client’s PeopleCloud product suite.

You have a strong machine learning and deep learning background and are passionate about transforming data into productionized ml models. You welcome the challenge of data science and are proficient in Python, Spark MLLib, Tensorflow, Keras, ML algorithms, Deep Neural Networks, big data, and cloud computing. You must be self-driven, take initiative and want to work in a collaborative, dynamic, busy, and innovative group.  You leverage your strong communication skills to represent our ML capabilities within the broader organization and provide mentorship for the team’s data scientists.  You are adept at taking complex concepts and simplify them so others can more easily understand the ML solutions we provided.


  • Leverage your experience in Machine Learning to help set the strategic direction for designing automated ML capabilities in the cloud
  • Design, implement, and validate productized analytic pipelines at scale using a variety of tools (AWS Sagemaker, Databricks, EMR, Spark, Python, Mllib, Git, etc.)
  • Liaison with other internal groups with The client to facilitate the development and adoption of our ML solutions
  • Identify new analytic capabilities and compare/contrast with existing methods
  • Develop an understanding of The client’s current analytic capabilities, proprietary datasets, and product capabilities
  • Mentor data scientists by providing feedback on algorithmic approaches and nurturing their career development
  • Participate fully in our collaborative culture by sharing knowledge, debating techniques, and conducting research to advance the collective knowledge and skills of the team


  • Advanced degree (Master’s/PhD) in Computer Science, Statistics, Math, Economics, or other quantitative discipline
  • A minimum of 8 years of hands-on experience delivering data science, machine learning, and AI solutions at scale to achieve specific business outcomes
  • A minimum of 5 years proven managerial or supervisory experience
  • Experience implementing a range of machine learning methods, from basic descriptive statistics, hypothesis testing and feature transformation to complex dimension reduction, supervised or unsupervised learning, and model tuning and validation
  • Knowledge of DNN models using TensorFlow v1.8 above, Keras, Pytorch, sequence modeling using RNNs/LSTMs is a plus
  • Prior project work in NLP is a plus
  • Coding proficiency in Python is must, any additional programming languages such as Scala, R, SQL, C, C++ is a plus
  • Project work using large scale distributed computing systems like Databricks Spark, EMR
  • Experience working with cloud infrastructure services like Amazon Web Services and MS Azure
  • Familiarity with code repositories and continuous integration (Git/Jenkins)
  • Exposure to scrum development methodology and tools (Jira/Confluence)
  • Ability to manage multiple data science projects concurrently
  • Excellent communication & interpersonal skills with an ability to communicate ideas, insights, and complicated analysis effectively at all organizational levels, include engineering leadership
  • Strong passion for understanding key business problems, bringing together data and algorithms to unearth deep insights into customer experiences

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    Mike Hanes