Data Scientist

Data Scientist

Requirements

• Bachelor’s or Master’s degree in Computer Science, Mathematics, Economics, Statistics, or related fields.
• 1–2 years of experience in data science or data analysis (including internships).
• Proficiency in SQL, Python, for data analysis and modeling.
• Hands-on experience with data validation and integrity checks.
• Exceptional communication skills with a focus on collaboration across different organizations and being able to communicate effectively with management.
• Knowledge of correlation modeling techniques, including: Logistic Regression, Decision Trees, Clustering (e.g., K-Means, DBSCAN)

Responsibilities

• Develop predictive models to identify high-value prospects and improve customer targeting for profitable acquisition.
• Discover correlation between customer touchpoints (Web interactions, emails, texts) and successful patient outcomes (New accounts created/retention %).
• Successfully tie back performance to attribution source.
• Analyze retention trends and develop strategies to enhance customer experience and lifetime value.
• Executes analytics and market research projects to obtain customer-patient insights.
• Delivers value-added statistical analyses of data across multiple systems and work with different business groups to solve operational reporting challenges (finance, billing, marketing, etc.).
• Communicates directly with key personnel to design and generate management-level reports.
• Ensure all strategies and implementations comply with HIPAA regulations and prioritize data privacy, preventing unauthorized access or misuse.
• Partner with marketing, operations, and IT teams to gather business requirements, define KPIs, and deliver actionable insights.
• Perform data validation with scheduled integrity checks to maintain accuracy and reliability.
• Define structure, rules, and governance policies for datasets.
• Update data tables to include additional data points & improve data load times.
• Create and implement an attribution model to properly weight how much each source is responsible for the final outcome (positive or negative).
• Design visualizations and dashboards using BI Tools like DOMO, Tableau, Power BI, etc.
• Manages KPIs and metrics tracking for all predictive projects.
• Perform clustering to identify characteristics and interactions correlated with positive or negative order/retention outcomes across product types.
• Convert unstructured data into structured formats for downstream applications.
• Build requirements for pipelines for seamless data integration into analytics and reporting tools.
• Develop and apply models for attribution, customer segmentation, and process automation.
• Implement machine learning techniques to enhance personalization and operational efficiency.
• Evaluate the impact of communication sequences on patient outcomes (e.g., web funnels, text/email messages, educational resources).

Preferred Qualifications

• Experience in data pipelining; API knowledge is a plus.
• Expertise in data visualization tools, particularly Power BI. Familiarity with modern product analytics tools is a bonus.
• Strong ability to present findings to both technical and non-technical stakeholders.
• Familiarity with setting up and interpreting A/B tests and attribution models.