Title: Lead Data Scientist- Houston, Tx.

Client: Professional Services Firm

Location: Houston, Texas (onsite)

Type: Direct-Hire

Salary: $180,000+

Summary:

• Candidate must be US Citizen, US Resident or Green Card Holder. This role resides in Houston, Texas and relocation is available for qualified candidates.
• Spearhead the design, development, 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 ML machine learning, optimization, and cluster analysis.
• Lead and help to develop a newly med 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 practice for AI & ML program management.

Duties:

• Translate business needs into analytics/reporting requirements to support data-driven decisions with required information & explain ability.
• 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 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/modelling 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 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 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.
• Python ecosystem preferred, R will be acceptable, 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, pattern recognition using variety of techniques such as decision trees, regressions, ensemble methods and boosting algorithms.
• Good understanding of programming best practices, building for re-use 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.