Lead Data Scientist

This is a fully remote position, open to applicants in United States.

📋 Description

• Oversee intricate data science projects related to audience decision-making, marketing decision science, pharmaceutical direct measurement, and employer analytics.

• Extract insights from extensive, complex datasets to comprehend user identity and the customer journey.

• Develop audience selection, segmentation, propensity, and lookalike models across marketing and content channels.

• Collaborate with marketing and content decision science teams on content creation, channel optimization, attribution, and incrementality measurement.

• Enhance measurement and modeling capabilities for pharmaceutical direct partners, focusing on prescription lift, audience targeting, and campaign efficacy.

• Assist with employer direct initiatives regarding member engagement, utilization, and adoption modeling.

• Enhance attribution and identity capabilities through NLP techniques to boost data quality.

• Create predictive models across classification, regression, and disambiguation challenges, managing the entire lifecycle from framing to monitoring and retraining.

• Spearhead experimentation strategies, including A/B testing, causal inference, and measurement frameworks.

• Establish and lead the technical roadmap for decision science capabilities.

• Offer technical guidance and mentorship to data scientists.

• Enhance data consistency, cleanliness, tooling, documentation, standards, model governance, reproducibility, and responsible AI practices.

⛳️ Requirements

• A minimum of 8 years of experience in data science, machine learning, operations research, or a similar quantitative field.

• Proven history of achieving measurable business results through implemented solutions.

• Strong preference for experience in audience modeling, marketing analytics, attribution, or identity resolution.

• Demonstrated technical leadership and capability to influence business strategy.

• An undergraduate degree or equivalent practical experience in a quantitative discipline such as Statistics, Mathematics, Computer Science, Economics, Operations Research, or Data Science.

• Profound understanding of machine learning, statistical modeling, optimization, causal inference, forecasting, experimentation, and predictive analytics.

• Proficient in Python and familiar with common data science libraries like pandas, NumPy, scikit-learn, PySpark, TensorFlow, or PyTorch.

• Solid working knowledge of databases and distributed data systems such as Redshift, PostgreSQL, and Spark/EMR.

• Familiarity with cloud platforms like AWS, GCP, or Azure and modern data platforms such as Databricks.

• Experience in deploying, monitoring, and operationalizing machine learning models using MLOps practices, experimentation platforms, feature stores, and model monitoring.

• Background in evaluating and utilizing AI/ML solutions, including generative AI and LLM technologies.

• Ability to excel in a fast-paced, rapidly changing environment.

• Excellent communication skills with the capability to convert technical insights into actionable recommendations.

• A collaborative and self-starting approach to work.

• Nice-to-have: experience in prescription, pharmacy, healthcare, marketing analytics, recommendation systems, reinforcement learning, optimization engines, advanced degrees, patents, publications, open-source projects, or industry thought leadership.

🏝️ Benefits

• Annual cash bonuses or commission.

• Annual equity grants for most positions.

• Medical, dental, and vision insurance.

• 401(k) with a company match.

• Employee Stock Purchase Plan (ESPP).

• Unlimited vacation.

• 13 paid holidays.

• 72 hours of sick leave.

• Mental wellness programs.

• Financial wellness programs.

• Fertility benefits.

• Generous parental leave.

• Pet insurance.

• Supplemental life insurance for you and your dependents.

• Company-paid short-term and long-term disability.

• Reasonable accommodations for candidates with disabilities.