Data Scientist

Net2Source

United States

about 2 months ago

Role details

Contract type

Temporary contract

Employment type

Full-time (> 32 hours)

Experience level

Experienced

Experience required

3 years minimum

Working hours

Regular working hours

Job source

arc.dev

Tech stack

Amazon Web Services Data Analysis Continuous Integration Python (Programming Language) Machine Learning Natural Language Processing Power BI Azure Machine Learning SQL Databases Tableau (Software) Feature Engineering Data Ingestion

+14 more

Large Language Models Prompt Engineering Deep Learning Electronic Medical Records Generative AI Containerization Core Data Information Technology Data Management Machine Learning Operations Restful APIs Api Management Docker Databricks

Job description

We are seeking a highly motivated Data Scientist to join a top-tier pharmaceutical client’s Global Data & Digital Innovation (GDDI) organization. This role bridges advanced machine learning, GenAI agent development, and production-grade MLOps pipelines to deliver actionable insights across Sales, Marketing, and Advanced Analytics teams.

Domain Expertise Required : This role focuses entirely on the Pharmaceutical Commercial Domain, combining advanced machine learning, GenAI agent development, and production-grade MLOps pipelines to drive commercial effectiveness across Sales, Marketing, and Advanced Analytics., Core Data Science & Commercial Strategy

Predictive Modeling: Develop and deploy models for patient events (line switches, initiation) and patient journey/longitudinal data analysis.
Next Best Action (NBA): Scale NBA solutions to optimize multichannel HCP engagement and segmentation.
Advanced ML: Apply regression, classification, and NLP techniques for commercial effectiveness.
Marketing Analytics: Create multi-touch attribution pipelines for customer journeys and promotional response modeling.
Stakeholder Support: Partner with Sales, Marketing, and Analytics teams to translate complex business problems into analytical solutions.

GenAI Integration

Integrate GenAI capabilities into commercial workflows (HCP engagement planning, content personalization, and GenAI interfaces for ML pipelines).

ML Engineering & MLOps

End-to-End Pipelines: Oversee build/maintenance of pipelines (data ingestion, feature engineering, training, evaluation, and deployment).
MLOps Best Practices: Implement model versioning, monitoring, retraining, and CI/CD integration.
Data Platforms: Work with large-scale healthcare datasets (Claims, EHR/EMR, CRM, digital engagement data) ensuring HIPAA compliance.

Requirements

Master’s Degree with 5-7+ years of experience OR PhD with 3-5+ years of experience.
Degree must be in Data Science, Computer Science, Statistics, Operations Research, Mathematics, or a related quantitative discipline.
Experience must be in data science, machine learning, or advanced analytics.
Preferred: Pharmaceutical/life sciences commercial analytics or healthcare consulting experience.

Technical Skills & Stack

Core DS: Python (preferred) or R; SQL; Supervised/Unsupervised ML algorithms; Statistical analysis and experimental design.
GenAI Stack: Hands-on experience with LLMs, Prompt Engineering, RAG architecture, and Agent-based AI systems (LangChain, MCP, A2A, AutoGen). Familiarity with Vector databases, embeddings, and API integrations.
MLOps & Infra: Experience with pipeline deployment and monitoring using Databricks, Azure ML, or AWS SageMaker. Knowledge of REST APIs, containerization (Docker), and CI/CD pipelines.
Visualization: Ability to build demo apps in Databricks; proficiency with BI tools (Power BI, Tableau); strong storytelling skills.