Lead Data Scientist

5 days ago


Houston, Texas, United States The Friedkin Group Full time
About the Role

The Friedkin Group is seeking a highly skilled Lead Data Scientist to spearhead the design, development, and implementation of advanced data science initiatives across business units. This role will focus on transforming innovative ideas into real-world solutions through the application of sophisticated analytical techniques.

Key Responsibilities
  • Translate business needs into analytics/reporting requirements to support data-driven decisions.
  • Keep abreast of the latest data science techniques and technologies, exploring and implementing innovative solutions to improve data analysis and business outcomes.
  • Communicate complex data insights in a clear and effective manner to stakeholders across the organization.
  • Manage use case design and build teams, providing guidance and feedback as they develop and operationalize data science models and algorithms.
  • Ensure analytical insights and products are embedded into business processes.
  • Guide and sign off on analytics/modelling approach, model deployment requirements, and quality assurance standards.
  • Provide input to the long-term plan for The Friedkin Group's Data Science team, including key focus areas, talent acquisition, and technology platforms.
  • Foster a culture of innovation and continuous improvement, leading the exploration and adoption of new data science technologies and methodologies.
Requirements
  • A master's degree or PhD in Computer Science, Statistics, Applied Mathematics, or a related field, with at least 5-7 years' experience in data science or a similar role.
  • Proficient in at least one analytical programming language relevant for data science, such as Python or R.
  • Expertise in advanced analytical techniques, including machine learning, optimization, and cluster analysis.
  • Experience with cloud computing environments and data/ML platforms.
  • Strong understanding of the Machine Learning lifecycle, including feature engineering, training, validation, scaling, deployment, monitoring, and feedback loop.
  • Experience in Supervised and Unsupervised Machine Learning, including classification, forecasting, anomaly detection, and pattern recognition.
Preferred Qualifications
  • Relevant certifications, such as Microsoft Certified: Azure Data Scientist Associate or AWS Certified Machine Learning.

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