Jobs at Baxter Planning

Own the strategy, roadmap, discovery, and commercialization of data-driven products and external data offerings. Partner with Data Engineering, Data Science, Design, Legal, Finance, and GTM teams to define data models, governance, privacy standards, APIs, and AI/ML enablement. Conduct customer and competitive research, translate insights into requirements, drive cross-functional execution, establish product metrics, and communicate technical capabilities as business outcomes.

Develop end-to-end machine learning solutions in Python, design experiments using AI and ML, maintain ML pipelines, collaborate with customers and product teams, and deploy solutions in AWS cloud environments.

The Marketing Operations Manager will enhance marketing performance and lead management through optimizing systems, improving data quality, and executing multi-channel campaigns. They will collaborate with teams, ensure accurate reporting, and drive operational excellence in the marketing organization.

Build and operate production-grade data pipelines, curated datasets, data marts, and BI-ready data models. Develop Python ETL/ELT and near-real-time AWS DMS ingestion, manage lakehouse schemas and incremental processing, and implement data quality and observability controls. Orchestrate workflows with Airflow and AWS services while contributing to CI/CD, infrastructure as code, architecture, and engineering best practices.

Generates qualified enterprise sales pipeline through outbound prospecting, account research, multichannel outreach, lead qualification, and opportunity nurturing. Partners with Sales and Marketing, engages senior decision-makers, tracks activity in Salesforce, follows up on campaigns and inbound leads, and continuously improves messaging. The role targets candidates with B2B SaaS or enterprise technology sales experience who can consistently meet activity and pipeline targets.

Lead quality strategy and automated test architecture for legacy and modernized systems. Build automated regression coverage, adopt AI-assisted testing tools, define CI/CD quality gates, own metrics and defect backlog triage, and grow a QA organization focused on automation and engineering-wide quality ownership.