Lead Data Scientist

Lead Data Scientist Location: Houston, TX 77002 Job Type: Full-Time Category: Non-Clinical Professional Vendor fee:$5000(Fixed Fee) Net Payment: 90 days Guarantee Period: 90 days About the Organization Our organization is committed to delivering high-quality, efficient services while creating exceptional experiences for the communities we serve. We value innovation, collaboration, data-driven decision-making, and continuous improvement. We are seeking an experienced Lead Data Scientist to provide advanced analytical expertise, lead complex data science initiatives, and deliver actionable insights that support strategic and operational decision-making. Position Summary The Lead Data Scientist will lead the analysis of complex and unstructured datasets using advanced statistical, analytical, and machine learning techniques. This individual will provide in-depth data insights for complex business problems, lead cross-functional projects, develop predictive models and algorithms, and translate analytical findings into clear and actionable recommendations for technical and non-technical stakeholders. The Lead Data Scientist will also provide technical guidance and mentorship to other data scientists and contribute to the development of data science capabilities across the organization. Key Responsibilities Lead high-priority and complex data science projects that have a significant organizational impact. Analyze structured and unstructured datasets using advanced statistical and analytical techniques. Develop custom data models, algorithms, and predictive solutions to address complex business problems. Apply machine learning and statistical models to key business metrics and operational challenges. Perform research, data analysis, modeling, data mining, visualization, and pattern analysis. Develop and test hypotheses and communicate findings in a clear, precise, and actionable manner. Maintain existing analytical models and evaluate model performance and goodness of fit. Identify opportunities to improve operational efficiency, productivity, scalability, and business outcomes through data. Work closely with cross-functional teams to identify, investigate, and resolve complex data issues. Gather requirements and translate business needs into analytical solutions. Provide technical leadership, coaching, and mentoring to other data scientists. Train broader teams on data science methodologies, tools, and developments. Assist in evaluating data science vendors, technologies, platforms, and tools. Lead multiple projects simultaneously while managing competing priorities and deadlines. Troubleshoot complex analytical and data-related issues and recommend appropriate solutions. Support strategic and operational decision-making through advanced data insights. Perform other duties and projects as assigned. Desired Skill Set Advanced data science and analytics Machine learning and predictive modeling Advanced statistical analysis SQL and database management Data mining and data visualization Structured and unstructured data analysis Statistical modeling and hypothesis testing Time-series forecasting Regression analysis Clustering and classification A/B testing Data storytelling and visualization Business and technical requirements gathering Project leadership and management Cross-functional collaboration Technical mentoring and team leadership Problem-solving and analytical reasoning Executive and stakeholder communication Minimum Qualifications Education Bachelor's degree in Science, Engineering, Computer Science, Mathematics, Statistics, or a related STEM field required. Master's degree in Data Science preferred. Licenses/Certifications None required. Experience, Knowledge & Skills Minimum 7 years of professional experience in Data Science . Experience in a hospital, healthcare, medical informatics, healthcare information technology, healthcare finance/revenue cycle, or Electronic Health Record (EHR) data environment is preferred. Strong business analytical skills, including process analysis, modeling, spreadsheets, and workflow analysis. Strong technical, mathematical, and analytical capabilities. Deep understanding of machine learning techniques, including: Clustering Decision tree learning Artificial neural networks Predictive modeling Classification techniques Advanced knowledge of statistical concepts and techniques, including: Regression Statistical testing Probability and distributions Hypothesis testing A/B testing Advanced understanding of the data science project lifecycle. Strong programming skills and experience with statistical analysis tools. Advanced knowledge of SQL and database management . Experience researching and resolving data issues involving large, complex, and incomplete datasets. Exceptional analytical and problem-solving skills. Ability to interpret and communicate complex analytical results. Strong project management skills and ability to independently manage multiple projects. Strong written and verbal communication skills with the ability to communicate effectively with technical and non-technical audiences. Ability to work with minimal supervision in a fast-paced, multidisciplinary environment. Strong customer-service orientation and commitment to producing high-quality analytical work. Ability to manage challenging stakeholder situations and provide effective solutions. Preferred Healthcare Experience Candidates with experience working with the following are highly desirable: Hospital or healthcare data Electronic Health Records (EHR) Healthcare IT Medical informatics Healthcare finance Revenue cycle data Clinical or operational healthcare analytics Ideal Candidate Profile The ideal candidate will be a senior-level data scientist with 7+ years of hands-on data science experience and strong expertise in advanced analytics, machine learning, statistical modeling, SQL, and predictive modeling. Candidates who have combined technical data science expertise with healthcare or hospital data experience are especially desirable. The successful candidate should be comfortable leading complex projects, mentoring other data scientists, working with large and incomplete datasets, and translating sophisticated analytical findings into practical business recommendations. #J-18808-Ljbffr