Data Scientist

Business Objective

Improve member engagement and campaign effectiveness using data-driven insights and machine learning

Translate analytical outputs into measurable business impact (ROI, KPIs)

Current State / Problem

Existing ML models and campaigns need stronger ownership, productionization, and evaluation

Gaps in data quality validation and production readiness

Need for better linkage between data analysis and business impact

Desired Outcome

Hire a senior-level data scientist who can independently build, deploy, and evaluate models while tying outputs to business outcomes

Improve campaign ideation, execution, and measurement with a stronger experimentation framework

Key Responsibilities

• Design, build, and deploy machine learning models at production scale
• Take ownership of existing ML models and improve performance and reliability
• Develop and launch data-driven marketing campaigns
• Partner with data engineers to productionize pipelines (DAGs, workflows)
• Conduct campaign sizing and impact analysis (ROI, KPI development, assumptions)
• Evaluate campaign effectiveness post-launch using proper experimentation methods
• Translate ambiguous business problems into analytical approaches and solutions
• Ensure data quality, validation, and production readiness of outputs
• Identify new campaign opportunities and propose innovative data-driven ideas
• Analyze model outputs and continuously optimize performance

Must Haves:

• Strong SQL and Python (independent development required)
• Experience building and deploying ML models in production environments
• Experience with data pipelines and workflow orchestration (e.g., DAGs)
• Working knowledge of cloud data platforms (Google Cloud Platform preferred)
• Data validation, quality assurance, and production readiness practices
• Experience with experimentation and A/B testing
• Strong analytical thinking for sizing problems and estimating business impact
• Ability to define and align on KPIs and success metrics
• Experience evaluating model effectiveness
• Strong business acumen (ability to connect data insights to business outcomes)
• Ability to work with ambiguous, open-ended problems
• Attention to detail (sanity checks, data validation)
• Strong collaboration with cross-functional teams (engineering + business)
• Proactive ideation and problem-solving mindset

Preferred Skills (Nice-to-Haves)

• Healthcare domain experience (especially member engagement/use cases)
• Prior campaign management or marketing analytics experience
• Familiarity with Claude or AI-assisted workflows
• Experience with campaign configuration platforms

Tech Stack

• SQL
• Python
• Google Cloud Platform (Google Cloud Platform)
• BigQuery
• Vertex AI
• DAG-based orchestration tools
• Visual Studio
• Claude (AI tooling, optional but desirable)