The Sr. Data Scientist, Healthcare will play a critical role in the design, development, and deployment of advanced data science and engineering solutions for Inmar’s Healthcare Intelligence and Advanced Analytics (HIAA) platform. This role bridges data science and engineering to enable scalable, high-performance analytics products that leverage healthcare data for actionable insights.

Reporting to the Director of Data Science, the Sr. Data Scientist will collaborate with data engineering, product, and compliance teams to build robust data pipelines, deploy machine learning models, and develop solutions that power predictive analytics. This hands-on role is ideal for someone who combines strong technical expertise with a passion for operationalizing data science models at scale.

Primary Accountabilities:

Data Science and Engineering (60%)

• Design and deploy machine learning models and advanced analytics pipelines to deliver scalable healthcare solutions.

• Collaborate with data engineering teams to design and implement robust, automated data pipelines that ensure data integrity, security, and performance.

• Build modular, reusable components for data transformation, feature extraction, and model deployment using modern cloud technologies (e.g., GCP, AWS).

• Leverage real-time data streaming and big data processing tools (e.g., Spark, Kafka) to support analytics products and services.

Cross-Functional Collaboration and Integration (30%)

• Work closely with engineering and product teams to ensure seamless integration of data science models into customer-facing applications and internal platforms.

• Partner with compliance and security teams to ensure that all data workflows and analytics models adhere to healthcare regulations, including HIPAA and HITRUST.

• Contribute to platform architecture discussions to align data science initiatives with broader technical and business objectives.

Innovation and Continuous Improvement (10%)

• Experiment with emerging tools and methodologies to optimize model performance and reduce deployment latency.

• Implement best practices for monitoring, retraining, and improving machine learning models in production environments.

• Mentor junior data scientists and data engineers, fostering a culture of continuous learning and technical excellence.

Qualifications:

Technical Skills and Experience:

• Education: Master’s degree in Data Science, Computer Science, or related field; PhD preferred.

• Experience:

  • 5+ years of experience in data science or machine learning, with at least 2 years focused on operationalizing models in production.

  • Strong programming skills in Python, SQL, and frameworks like TensorFlow, PyTorch, or Scikit-learn.

  • Proven ability to design and implement data pipelines using big data tools (e.g., Spark, Hadoop) and cloud platforms (e.g., GCP, AWS).

  • Experience deploying machine learning models via APIs or containerized frameworks like Docker and Kubernetes.

• Healthcare Focus: Knowledge of healthcare data structures (e.g., claims, EHR) and compliance requirements, including HIPAA and HITRUST.

Soft Skills:

• Problem-Solving: Capable of diagnosing complex data challenges and delivering scalable solutions.

• Collaboration: Builds strong partnerships across engineering, product, and compliance teams.

• Technical Communication: Effectively translates technical concepts into actionable strategies for cross-functional teams.

• Adaptability: Quickly learns and applies new technologies to evolving business needs.

Competencies:

• Engineering-Driven Data Science: Strong emphasis on building scalable, production-grade solutions that bridge data science and engineering.

• Operational Excellence: Implements best practices for pipeline automation, model monitoring, and continuous optimization.

• Innovation: Proactively evaluates emerging technologies to improve performance and efficiency.

• Leadership: Mentors team members and fosters a culture of technical growth and excellence.

Key Outcomes:

• Design and deploy at least three scalable machine learning pipelines annually, powering key analytics products.

• Optimize operational efficiency, reducing data processing and model deployment times by 30%.

• Build reusable tools and frameworks for data science teams, improving overall productivity by 20%.

• Ensure full compliance with regulatory standards across all data science workflows.

As an Inmar Associate, You Will:

• Drive technical innovation and scalability across healthcare data science solutions.

• Collaborate with cross-functional teams to integrate data science initiatives into broader organizational goals.

• Foster a culture of engineering-focused excellence in data science processes and model development.

We are an Equal Opportunity Employer, including disability/vets.