Data Scientist

Position Summary
The Marketing Data Scientist is the predictive intelligence engine of TCMD's Marketing and Market Access organization. The primary work is finding connections in TCMD's data that no one has looked for yet, building predictive models, and translating validated models into forward-looking tools. This individual synthesizes insights from Tactile’s internal and external data platforms to develop and explore hypotheses for growth. The role's mission is to surface predictive insights from these systems that inform commercial strategy before decisions are finalized. This role collaborates closely with marketing and market access leadership along with sales excellence and commercial leadership.

Accountabilities & Responsibilities
• Exploratory analysis, hypothesis generation, feature engineering, model construction, and validation
• Build and validate predictive models using appropriate machine learning and statistical methodologies
• Translating validated models into forward-looking dashboards or automated scoring systems that are consumed with ease by stakeholders
• Partner across the marketing organization to develop campaign lift attribution; building causal inference models isolating incremental referral lift from specific marketing programs
• Develop predictive analytics supporting payer targeting and coverage expansion opportunities
• Train commercial team users on how to interpret and act on model outputs and the specific decisions the model is designed to support
• Communicate within marketing and market access on status of model pipeline and backlog; routinely collect voice of internal stakeholder needs to drive continuous improvement in data driven decision making
• Manage assigned projects to completion on time, within scope, and within budget.
• Other duties as assigned.

Qualifications

Required:
• Bachelor's degree in data science, statistics, mathematics, computer science, economics, or a quantitative field with strong statistical foundations
• 4-7 years applied data science or machine learning experience, applied in commercial or operational environments
• Experience creating predictive models for non-data-scientists to make real commercial or operational decisions
• Comfort with messy healthcare commercial data, intellectual curiosity, and the communication discipline to translate technical findings into commercial language
• Expert-level modern data science skills in Python and SQL working with structured data and machine-learning frameworks; version-controlled code development and deployment
• Ability to transform messy, real-world healthcare data with missing values, inconsistent coding, and multiple granularities into reliable predictive model inputs
• Working knowledge of Salesforce CRM architecture, healthcare claims data, Power BI/Fabric deployment environments

Preferred:
• Master's or PhD in quantitative field
• Understanding of referral-based commercial models, payer coverage dynamics, prior authorization processes, and DME/medical device reimbursement
• Survival analysis experience — has applied time-to-event modeling in a commercial context (e.g., customer churn, time-to-conversion, time-to-renewal). Particularly relevant for funnel stage duration modeling and HCP churn prediction
• Salesforce data architecture familiarity — understands the Salesforce object model well enough to write efficient queries and build reliable features from CRM data without requiring a Salesforce administrator to extract data
• Power BI or Tableau development experience sufficient to deploy model scoring outputs as operational dashboards
• Experience in a B2B2C or referral-based commercial model where the customer and the end user are different