Lead Data Scientist (AI)

TL;DR

Lead Data Scientist (AI): Building and deploying predictive models, audience decisioning systems, and marketing measurement capabilities for a healthcare affordability platform with an accent on machine learning, causal inference, experimentation, and responsible AI. Focus on connecting fragmented identity and behavioral signals, optimizing user and partner decision-making, and operationalizing reliable models through modern MLOps practices.

Location: Remote within the United States; compensation varies by work location, including San Francisco, Seattle, New York, Santa Monica, and other U.S. locations.

Salary: $151,000–$323,000, depending on work location, skills, experience, and qualifications.

Company

hirify.global is a U.S. healthcare marketplace and prescription savings platform that helps consumers, employers, health plans, and healthcare partners access affordable medications and healthcare information.

What you will do

• Lead data science initiatives across audience decisioning, marketing analytics, pharma measurement, and employer analytics.
• Build segmentation, propensity, lookalike, identity, attribution, and predictive models using large behavioral, prescription, claims, and partner datasets.
• Design experimentation strategies, A/B tests, causal inference methods, and incrementality measurement frameworks.
• Own the machine learning lifecycle from problem framing and feature engineering through deployment, monitoring, and retraining.
• Define the technical roadmap for decision science, responsible AI, model governance, reproducibility, and shared data tooling.
• Provide technical leadership and mentorship while partnering with data scientists, engineers, product managers, marketing teams, and business stakeholders.

Requirements

• 8+ years of experience in data science, machine learning, operations research, or a related quantitative field.
• Technical leadership experience and a record of delivering productionized solutions with measurable business impact.
• Strong knowledge of machine learning, statistical modeling, optimization, causal inference, forecasting, experimentation, and predictive analytics.
• Expertise in Python and data science libraries such as pandas, NumPy, scikit-learn, PySpark, TensorFlow, or PyTorch.
• Experience with databases, distributed data systems, cloud platforms, Databricks, and MLOps practices including feature stores and model monitoring.
• Quantitative degree or equivalent practical experience, strong communication skills, and the ability to influence technical and business stakeholders.

Nice to have

• Experience in prescription, pharmacy, healthcare, marketing analytics, audience segmentation, attribution, or incrementality measurement.
• Experience supporting pharma manufacturers, employers, benefits partners, or B2B analytics initiatives.
• Background in recommendation systems, reinforcement learning, optimization engines, or decision science.
• Advanced quantitative degree, patents, publications, open-source contributions, or industry thought leadership.

Culture & Benefits

• Remote work within the United States.
• Medical, dental, and vision insurance with company-paid disability coverage.
• 401(k) with company match, employee stock purchase plan, annual equity grants, and additional compensation programs.
• Unlimited vacation, 13 paid holidays, 72 hours of sick leave, and generous parental leave.
• Mental wellness, financial wellness, fertility, pet insurance, and supplemental life insurance benefits.
• Inclusive workplace focused on diverse perspectives, responsible AI, security, and professional excellence.