Data Scientist

Data Scientist II

Location: Miami, Florida, United States (On-site)

Industry: Hospitals and Health Care

Work Setting: On-site

Are you passionate about using data science, machine learning, and AI to transform complex data into meaningful insights that drive better outcomes and business decisions?

We are seeking a Data Scientist II to leverage advanced analytics, statistical modeling, and machine learning techniques to solve complex challenges across healthcare, operational, and business domains. This role will work closely with cross-functional teams to develop predictive models, uncover actionable insights, and build scalable data solutions that support data-driven decision-making and innovation.

Key Responsibilities

• Apply machine learning, artificial intelligence, and natural language processing techniques to analyze large volumes of structured and unstructured data.
• Develop predictive and prescriptive models using diverse data sources to identify trends, improve decision-making, and support strategic initiatives.
• Design and maintain scalable data pipelines that integrate information from multiple systems and sources.
• Utilize graph-based data modeling and advanced analytics techniques to discover hidden relationships and generate deeper insights.
• Partner with data engineering, technology, and business teams to deploy analytical solutions into production environments.
• Develop and validate predictive models for forecasting, risk assessment, optimization, and performance improvement initiatives.
• Ensure data governance, privacy, security, and compliance standards are followed throughout all analytical activities.
• Collaborate with stakeholders to understand business requirements and translate them into effective analytical and technical solutions.
• Document methodologies, model development processes, validation procedures, and analytical findings to ensure reproducibility and transparency.
• Provide technical guidance, knowledge sharing, and mentorship on data science best practices and analytical methodologies.

Required Qualifications

• Bachelor's degree in Data Science, Statistics, Computer Science, Mathematics, Analytics, Bioinformatics, or another quantitative discipline, or equivalent practical experience.
• 3+ years of experience in data science, machine learning, analytics, or related fields.
• Experience working with large and complex datasets from multiple business systems and data sources.
• Demonstrated success applying statistical analysis, predictive modeling, and machine learning techniques to solve real-world business problems.
• Experience transforming data into actionable insights that support operational and strategic objectives.

Preferred Qualifications

• Master's degree or higher in a quantitative or technical field.
• Experience with advanced analytics, artificial intelligence, and machine learning model deployment.
• Exposure to graph databases, vector databases, and knowledge graph technologies.
• Experience working with cloud-based data platforms and modern data architectures.
• Familiarity with large language models (LLMs), generative AI technologies, and natural language processing frameworks.
• Experience supporting advanced analytics initiatives involving complex, multi-source datasets.

Technical Skills

• Strong proficiency in Python, R, SQL, and data analysis libraries.
• Expertise in statistical modeling, machine learning algorithms, predictive analytics, and data visualization techniques.
• Experience working with cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Knowledge of distributed computing frameworks and large-scale data processing technologies.
• Familiarity with API integrations, data engineering concepts, and data pipeline development.
• Experience using modern analytics, reporting, and visualization tools.

Desired Skills

• Strong analytical thinking and problem-solving capabilities.
• Ability to translate business objectives into scalable technical solutions.
• Excellent communication skills with the ability to present complex findings to both technical and non-technical audiences.
• Strong attention to detail and commitment to data quality, accuracy, and integrity.
• Ability to manage multiple priorities and projects in a fast-paced environment.
• Collaborative mindset with experience working across business, technology, and research-focused teams.
• Self-motivated professional who can work independently while contributing effectively within a team environment.