Senior Data & Analytics Engineer - Healthcare Data & Machine Learning

Location: Deerfield, IL – Hybrid (4 days onsite / 1 day remote)
Duration: 6-month contract with potential for extension or full-time conversion
Schedule: Monday–Friday, 8:00 AM–5:00 PM CST
Compensation: Competitive hourly W2 rate ($51–$53.50/hour), access to healthcare & dental insurance plan of choice

About the role

We are seeking an experienced Senior Data & Analytics Engineer to support advanced analytics, data science, and business intelligence initiatives involving large, complex datasets and multifaceted business questions. This role sits at the intersection of data science, advanced analytics, data engineering, and business intelligence, with a strong emphasis on transforming complex data into actionable insights that support operational, business, and patient-related decision-making.

The ideal candidate will bring approximately 5+ years of data science, advanced analytics, or related experience, including at least 2 years of hands-on data pipeline experience. Strong proficiency with SQL and Python is essential, along with experience working in a cloud environment such as AWS, GCP, or Azure. Experience working with healthcare, pharmacy, medical, claims, or other health-related data is highly valued.

The successful candidate will combine strong technical and statistical capabilities with the business acumen and communication skills necessary to explain analytical findings clearly to non-technical stakeholders and help translate those findings into meaningful business decisions.

Responsibilities

• Analyze large, complex datasets to answer challenging business, operational, healthcare, and patient-related questions.
• Navigate multiple data environments and determine the appropriate data sources, methodologies, and analytical approaches needed to address business needs.
• Use SQL and Python to extract, manipulate, transform, analyze, and interpret complex datasets.
• Design, develop, and support data pipelines that provide reliable data foundations for advanced analytics and data science initiatives.
• Perform advanced statistical analysis, statistical modeling, and, where appropriate, machine-learning analysis to identify trends, relationships, patterns, and predictive insights.
• Evaluate business problems and identify the underlying drivers and root causes behind performance trends, operational issues, and other analytical findings.
• Develop and refine KPIs, quality measures, analytical frameworks, and performance metrics that enable effective business decision-making.
• Support quality management initiatives through data analysis, measurement, validation, and identification of improvement opportunities.
• Translate ambiguous or complex business questions into structured analytical approaches and measurable outcomes.
• Develop statistical or predictive models appropriate to the business problem and clearly articulate why specific modeling methodologies were selected.
• Evaluate model results and analytical findings to determine their practical significance and business relevance.
• Work with healthcare, pharmacy, medical, claims, patient, or related datasets to generate meaningful informatics and intelligence.
• Utilize cloud-based data environments such as AWS, GCP, or Azure to access, process, and analyze large datasets.
• Partner with business stakeholders, analysts, data scientists, engineers, and other technical teams to understand analytical needs and develop appropriate solutions.
• Present analytical findings in a clear, concise manner that enables non-technical business stakeholders to understand the implications and make informed decisions.
• Maintain strong data quality, governance, security, and documentation standards throughout the analytical lifecycle.

Requirements

• Bachelor’s degree required, preferably in Statistics, Data Science, Mathematics, Computer Science, Economics, Engineering, Biostatistics, or another quantitative or analytical discipline.
• Approximately 5+ years of professional experience in data science, advanced analytics, statistical analysis, business analytics, or a related field.
• At least 2+ years of hands-on experience developing or working with data pipelines.
• Advanced proficiency with SQL for querying, manipulating, and analyzing complex datasets.
• Strong hands-on Python experience for data analysis, statistical analysis, modeling, and data manipulation.
• Experience working within at least one major cloud environment, including AWS, GCP, or Azure.
• Strong knowledge of statistical analysis and quantitative problem-solving methodologies.
• Experience developing statistical, predictive, or machine-learning models OR a strong academic foundation in statistics/statistical modeling.
• Ability to explain models previously developed, including the business problem being addressed, the methodology selected, and the rationale behind the modeling approach.
• Demonstrated ability to work with large, complex datasets across multiple systems or data environments.
• Experience translating broad or ambiguous business questions into structured analytical approaches.
• Strong problem-solving skills with the ability to move beyond surface-level findings and identify root causes and underlying business issues.
• Ability to develop KPIs, quality measures, performance metrics, and other analytical frameworks.
• Excellent communication skills with the ability to translate complex technical and analytical findings into clear, actionable insights for non-technical business stakeholders.

Preferred Qualifications

• Experience working with healthcare, pharmacy, medical, claims, patient, clinical, or related healthcare data.
• Previous experience supporting healthcare analytics, health informatics, quality management, patient-focused analytics, or pharmacy-related initiatives.
• Experience analyzing large-scale retail, consumer, or credit-card/transactional datasets.
• Advanced educational background in Statistics, Biostatistics, Mathematics, Data Science, Economics, or another highly quantitative discipline.
• Practical experience developing machine-learning models using Python-based analytical or modeling libraries.
• Experience with Databricks, Spark, Snowflake, Teradata, or other large-scale data and analytics platforms.
• Experience supporting analytics initiatives where findings directly influence business strategy, operational improvements, quality measures, or patient-related decisions.
• Experience developing healthcare dashboards or self-service analytics using Tableau, Databricks Dashboards, Power BI, or comparable BI platforms.