Lead Data Scientist

3 days ago

Houston, United States ZipRecruiter Full time

Job Description Title: Lead Data Scientist - Houston, Tx. Client: Professional Services Firm Location: Houston, Texas (onsite) Type: Direct-Hire Salary: $180,000+ Summary: Candidate must be a US citizen or Green Card holder. This role resides in Houston, Texas, and relocation is available for qualified candidates. Spearhead the design, development, and implementation of advanced data science initiatives across business units. Directly align data science campaigns with strategic corporate objectives encompassing the digital transformation of innovative ideas into real-world solutions. Work with robust applications of sophisticated analytical techniques such as machine learning, optimization, and cluster analysis. Lead and help to develop a newly formed data scientists taskforce in delivering impactful analytical solutions, ensuring these innovations are seamlessly embedded into business operations. Drive decision-making, enhance operational efficiency, and foster a culture of continuous improvement and innovation. This role plays a significant part in setting the AI & ML agenda, including working with business units to define potential opportunities, and defining standards and best practices for AI & ML program management. Duties: Translate business needs into analytics/reporting requirements to support data-driven decisions with required information & explainability. Keep abreast of the latest data science techniques and technologies. Explore and implement innovative solutions to improve data analysis, modeling capabilities, and business outcomes. Communicate complex data insights in a clear and effective manner to stakeholders across the organization, including non-technical audiences. Advocate for the importance and value of data-driven decision making. Manage use case design and build teams on a day-to-day basis, providing guidance and feedback as they develop and operationalize data science models and algorithms to solve complex business problems. Ensure analytical insights and products are embedded into business processes. Ensure use case models/analytics are supported, maintained, and improved (as needed) post-development and launch. Guide and sign off on analytics/modeling approach, model deployment requirements, and quality assurance standards with input from use case teams and business leadership. Provide input to the long-term plan for the Data Science team, including key focus areas, talent acquisition, input to technology platforms, and interaction model with the rest of the organization. Foster a culture of innovation and continuous improvement and lead the exploration and adoption of new data science technologies and methodologies to contribute to the advancement of analytics expertise. Work with a wide landscape of business and technical stakeholders to proactively identify applicable new technologies and opportunities and detail and communicate how they can deliver measurable business value. Own the analytics solution portfolio, including model maintenance and improvements over time, and will directly supervise one or more employees. Requirements: 7+ years of experience with at least one analytical programming language relevant for data science. Demonstrated ability to lead and manage data science projects, including managing workflow and priorities, to ensure timely delivery of projects with high-quality outcomes. Proven track record of recruiting, training, and retaining a skilled data science team, identifying talent gaps, and addressing them. Proficiency in the Python ecosystem; R is acceptable, along with machine learning libraries & frameworks. Versed in TensorFlow, PyTorch, scikit-learn, and familiar with data processing and visualization tools (e.g., SQL, Tableau, Power BI). Expertise in advanced analytical techniques (e.g., descriptive statistics, machine learning, optimization, pattern recognition, cluster analysis, etc.) Experience with cloud computing environments (AWS, Azure, or GCP) and Data/ML platforms (Databricks, Spark). Strong understanding of the Machine Learning lifecycle - 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 using a variety of techniques such as decision trees, regressions, ensemble methods, and boosting algorithms. Good understanding of programming best practices, building for reuse and highly automated CI/CD pipelines. Proven track record of leading cross-functional teams to successfully deliver complex data-driven projects. Excellent problem-solving and analytical skills, with the ability to translate complex technical details into understandable business insights. Education: Master’s degree or PhD in Computer Science, Statistics, Applied Mathematics, or a related field, with at least 5 – 7 years’ experience in data sciences. Relevant certifications such as Microsoft Certified: Azure Data Scientist Associate or AWS Certified Machine Learning are advantageous. #J-18808-Ljbffr

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