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Data Scientist
Job Description
Job Title: Data Scientist III
Location: 100% Remote
Contract Duration: 12 Months
Pay Range: $80.00 - $100.72 USD hourly on W2
Job Description:
We are seeking a highly motivated Data Scientist to join the Global Data & Digital Innovation (GDDI) organization within the pharmaceutical commercial domain. This role focuses on building data science and AI-driven solutions, including predictive patient event modeling, Next Best Action (NBA) engines for HCP engagement, and GenAI-powered decision agents to enhance commercial effectiveness.
The ideal candidate will combine strong machine learning expertise with experience in GenAI agent development and scalable ML pipelines, enabling actionable insights for stakeholders across GDDI, Sales, Marketing, Sales Analytics, and Advanced Analytics functions.
Key Responsibilities
Develop and deploy predictive models for patient events (line switches, initiation)
Scale Next Best Action (NBA) solutions to optimize HCP engagement strategies across channels to various products
Apply advanced ML techniques including regression, classification, and NLP techniques
Create multi touch attribution pipelines for the customer journeys and optimization
Integrate GenAI capabilities into commercial workflows such as:
HCP engagement planning
Content personalization
Gen AI interfaces for ML pipelines
ML Engineering & Pipeline Development
Oversee build and maintenance of end-to-end ML pipelines including:
Data ingestion, feature engineering, model training, evaluation, and deployment
Implement MLOps best practices:
Model versioning, monitoring, and retraining pipelines
CI/CD integration for scalable deployment
Work with modern data platforms (e.G., Databricks, AWS)
Commercial Strategy & Stakeholder Support
Partner with Sales, Marketing, and Sales Analytics teams to translate business problems into analytical solutions
Deliver actionable insights and recommendations to senior stakeholders
Collaborate with:
Advanced Analytics teams (modeling and experimentation on alerts)
Data Engineering teams (data pipelines and infrastructure)
Business stakeholders (Sales, Marketing, Market Access)
Act as a bridge between technical and business teams, ensuring adoption of advanced analytics and AI solutions
Data Management & Compliance
Work with large-scale healthcare datasets such as:
Claims, EHR/EMR, CRM, and digital engagement data
Ensure compliance with data privacy and regulatory standards (e.G., HIPAA)
Required Qualifications
Education
Master s or PhD in:
Data Science
Computer Science
Statistics
Operations Research
Mathematics or a related quantitative discipline
Experience
5-7+ years (Master s) or 3 5+ years (PhD) in:
Data science, machine learning, or advanced analytics
Pharmaceutical / life sciences commercial analytics preferred
Healthcare analytics or consulting experience
Technical Skills
Core Data Science
Proficiency in:
Python (preferred) or R
SQL
Strong understanding of:
Machine learning algorithms (supervised/unsupervised learning)
Statistical analysis and experimental design
GenAI & Modern AI Stack
Hands-on experience with:
Large Language Models (LLMs) and GenAI frameworks
Prompt engineering and RAG architecture
Agent-based AI systems (e.G., LangChain, MCP, A2A, AutoGen)
Familiarity with:
Vector databases and embeddings
API-based AI integrations
ML Engineering / MLOps
Experience with:
Overseeing ML pipelines (training deployment monitoring)
Tools such as Databricks, Azure ML, AWS SageMaker
Knowledge of:
Model deployment, REST APIs, containerization (Docker)
CI/CD pipelines for ML systems
Visualization & Communication
Ability to build apps for demo purposes using Databricks
Experience with BI tools (Power BI, Tableau)
Strong storytelling skills to communicate insights effectively
Domain Expertise (Preferred)
Pharmaceutical commercial domain experience, including:
Patient journey and longitudinal data analysis
HCP targeting and segmentation
Omnichannel marketing analytics and campaign optimization
Experience in:
Next Best Action (NBA) frameworks
Sales force effectiveness
Promotional response modeling especially Multi- touch Attribution
Key Competencies
Strong problem-solving mindset with business acumen
Ability to bridge AI innovation with commercial impact
Excellent stakeholder management and communication skills
Experience working in cross-functional, global teams
High attention to detail and commitment to quality
What Success Looks Like
Delivering scalable AI/ML and GenAI solutions that drive commercial insights
Enabling smarter, real-time decision-making for Sales and Marketing teams
Successfully deploying GenAI agents and production-grade ML pipelines
Becoming a trusted partner within the Global Data & Digital Innovation organization
Location: 100% Remote
Contract Duration: 12 Months
Pay Range: $80.00 - $100.72 USD hourly on W2
Job Description:
We are seeking a highly motivated Data Scientist to join the Global Data & Digital Innovation (GDDI) organization within the pharmaceutical commercial domain. This role focuses on building data science and AI-driven solutions, including predictive patient event modeling, Next Best Action (NBA) engines for HCP engagement, and GenAI-powered decision agents to enhance commercial effectiveness.
The ideal candidate will combine strong machine learning expertise with experience in GenAI agent development and scalable ML pipelines, enabling actionable insights for stakeholders across GDDI, Sales, Marketing, Sales Analytics, and Advanced Analytics functions.
Key Responsibilities
Develop and deploy predictive models for patient events (line switches, initiation)
Scale Next Best Action (NBA) solutions to optimize HCP engagement strategies across channels to various products
Apply advanced ML techniques including regression, classification, and NLP techniques
Create multi touch attribution pipelines for the customer journeys and optimization
Integrate GenAI capabilities into commercial workflows such as:
HCP engagement planning
Content personalization
Gen AI interfaces for ML pipelines
ML Engineering & Pipeline Development
Oversee build and maintenance of end-to-end ML pipelines including:
Data ingestion, feature engineering, model training, evaluation, and deployment
Implement MLOps best practices:
Model versioning, monitoring, and retraining pipelines
CI/CD integration for scalable deployment
Work with modern data platforms (e.G., Databricks, AWS)
Commercial Strategy & Stakeholder Support
Partner with Sales, Marketing, and Sales Analytics teams to translate business problems into analytical solutions
Deliver actionable insights and recommendations to senior stakeholders
Collaborate with:
Advanced Analytics teams (modeling and experimentation on alerts)
Data Engineering teams (data pipelines and infrastructure)
Business stakeholders (Sales, Marketing, Market Access)
Act as a bridge between technical and business teams, ensuring adoption of advanced analytics and AI solutions
Data Management & Compliance
Work with large-scale healthcare datasets such as:
Claims, EHR/EMR, CRM, and digital engagement data
Ensure compliance with data privacy and regulatory standards (e.G., HIPAA)
Required Qualifications
Education
Master s or PhD in:
Data Science
Computer Science
Statistics
Operations Research
Mathematics or a related quantitative discipline
Experience
5-7+ years (Master s) or 3 5+ years (PhD) in:
Data science, machine learning, or advanced analytics
Pharmaceutical / life sciences commercial analytics preferred
Healthcare analytics or consulting experience
Technical Skills
Core Data Science
Proficiency in:
Python (preferred) or R
SQL
Strong understanding of:
Machine learning algorithms (supervised/unsupervised learning)
Statistical analysis and experimental design
GenAI & Modern AI Stack
Hands-on experience with:
Large Language Models (LLMs) and GenAI frameworks
Prompt engineering and RAG architecture
Agent-based AI systems (e.G., LangChain, MCP, A2A, AutoGen)
Familiarity with:
Vector databases and embeddings
API-based AI integrations
ML Engineering / MLOps
Experience with:
Overseeing ML pipelines (training deployment monitoring)
Tools such as Databricks, Azure ML, AWS SageMaker
Knowledge of:
Model deployment, REST APIs, containerization (Docker)
CI/CD pipelines for ML systems
Visualization & Communication
Ability to build apps for demo purposes using Databricks
Experience with BI tools (Power BI, Tableau)
Strong storytelling skills to communicate insights effectively
Domain Expertise (Preferred)
Pharmaceutical commercial domain experience, including:
Patient journey and longitudinal data analysis
HCP targeting and segmentation
Omnichannel marketing analytics and campaign optimization
Experience in:
Next Best Action (NBA) frameworks
Sales force effectiveness
Promotional response modeling especially Multi- touch Attribution
Key Competencies
Strong problem-solving mindset with business acumen
Ability to bridge AI innovation with commercial impact
Excellent stakeholder management and communication skills
Experience working in cross-functional, global teams
High attention to detail and commitment to quality
What Success Looks Like
Delivering scalable AI/ML and GenAI solutions that drive commercial insights
Enabling smarter, real-time decision-making for Sales and Marketing teams
Successfully deploying GenAI agents and production-grade ML pipelines
Becoming a trusted partner within the Global Data & Digital Innovation organization
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