Data Science Specialist

🚀 We’re Hiring: Senior Applied Data Scientist – Commercial Pharma
📍 Location: Remote, United States
📌 Preferred locations: Boston, New York, Chicago, or Texas
💼 Experience: 10+ years in applied data science
✈️ Travel: Approximately 10%, including occasional client-site visits and quarterly team meetings
🛂 Work Authorization: US Citizens or Green Card holders only
Visa sponsorship is not available.
About the Company
Our client is an innovative, fast-growing healthcare AI company developing technology that brings predictive analytics and AI-powered workflows into real-world healthcare operations. Its platform combines large-scale data with machine learning to help healthcare organizations identify patient needs, improve targeting, and turn complex data into actionable insights.
The company works across healthcare, life sciences, and enterprise health solutions, with a strong focus on production-ready AI, measurable customer outcomes, and scalable technology. This is an opportunity to join a collaborative, entrepreneurial environment where technical ownership and direct stakeholder engagement are highly valued.
About the Role
We’re looking for a hands-on Senior Applied Data Scientist to lead commercial pharma analytics initiatives and build predictive models that support pharmaceutical commercial strategy, HCP targeting, and business effectiveness.
You’ll work directly with stakeholders, own technical delivery from problem definition through production, and help translate complex data into measurable business outcomes.
Key Responsibilities
Own client engagements end to end, from scoping and data integration through solution architecture, model development, production deployment, and ongoing optimization.
Build predictive models for HCP/HCO segmentation, physician propensity, HCP targeting, campaign optimization, and commercial pharmaceutical analytics.
Design and defend commercially relevant studies, including precision targeting to support patient recruitment for Phase II/III clinical trials.
Deploy and monitor production ML models on cloud platforms such as AWS or GCP, applying MLOps best practices.
Develop scalable data and ML pipelines, including ETL/ELT, PySpark/Spark workflows, and processing of structured and unstructured data.
Lead technical working sessions with senior stakeholders, translate business needs into analytical solutions, and clearly communicate and defend methodologies.
Manage implementations from pilot through production and help shape product improvements based on recurring client needs.
Collaborate across teams in a fast-paced environment, balancing hands-on technical work, client engagement, and strategic problem-solving.
Required Qualifications
10+ years of applied data science experience.
Commercial pharma or biopharma experience, ideally in HCP targeting, field-force enablement, sales effectiveness, or commercial/marketing analytics.
Proven hands-on experience taking predictive models from study design and development through production deployment and maintenance.
Strong Python and SQL skills.
Experience with AWS or GCP and production ML workflows.
Experience with data and ML pipelines using tools such as pandas, scikit-learn, Airflow, Spark, or PySpark.
Strong client-facing communication, stakeholder management, and analytical presentation skills.
An entrepreneurial mindset with the ability to take ownership in a lean, fast-moving environment.
Preferred Qualifications
Experience with rare diseases, specialty pharma, or pharmaceutical launches.
Experience in HCP propensity modeling, segmentation, campaign optimization, and promotional effectiveness.
Exposure to LLM-powered systems, generative AI, or agentic AI.
Experience in a startup, founding team, or high-growth technology environment.
Ability to translate technical solutions into scalable products and measurable customer outcomes.
Interested?
If you combine commercial pharmaceutical analytics, hands-on machine learning, and end-to-end production delivery, I’d love to connect.
📩 Send me a direct message to learn more about the opportunity.
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Work arrangement
Yes