Top Tech Jobs & Startup Jobs in Boston, MA

Designs conceptual, logical, and physical data models for R&D research and clinical domains. Translates scientific and business requirements into reusable, scalable, AI-ready data structures that support analytics, machine learning, data integration, and standardized workflows. Partners with domain experts, data engineers, architects, data scientists, and governance teams to apply enterprise standards, schemas, ontologies, metadata, quality rules, and lineage. Promotes shared data assets and ensures models are implementable, interoperable, and aligned with evolving R&D needs.

Leads consumer omnichannel strategy for a psoriatic arthritis pharmaceutical launch and growth. Owns patient journeys across websites, CRM, email, SMS, digital media, social, and experience tools. Directs website, Salesforce Marketing Cloud, SEO/GEO, paid media, social content, influencer strategy, agency partnerships, audience targeting, and measurement. Collaborates with brand, data, technology, analytics, sales, patient services, market access, and other teams to deliver compliant, personalized, data-driven patient engagement and conversion experiences.

Leads enterprise-level Global Capability Management initiatives, overseeing portfolio planning, governance, execution, financial discipline, risk management, business continuity, and executive reporting. Partners with senior stakeholders across functions and geographies to align priorities, resolve dependencies, manage resources and budgets, and enable decisions. Establishes integrated program plans, governance routines, portfolio dashboards, and accountability mechanisms while driving sustainable transformation outcomes across global operating models and capability center environments.

Owns the strategic vision, roadmap, delivery, adoption, and business value of Takeda’s AI-native discovery automation and robotics product family. Leads cross-functional teams across scientific, digital, data, computational, automation, and technology functions; defines AI-enabled laboratory workflows; prioritizes investments; guides platform and integration choices; manages stakeholders and external partners; and drives measurable improvements in laboratory throughput, data quality, scientific impact, and operating efficiency.

Serves as a finance business partner for oncology sales, analytics, strategy, and business operations functions. Supports Solid Tumor and Hematology franchises through FP&A, forecasting, budgeting, OpEx management, resource allocation, scenario analysis, financial modeling, dashboards, and performance reporting. Provides financial insights, narratives, and recommendations to guide investment decisions and business strategy. Partners cross-functionally during planning cycles and coaches junior colleagues while managing workstreams.

Own the technical roadmap, backlog, architecture direction, and delivery execution for Takeda’s AI-native large molecule discovery software products. Partner with business, scientific, engineering, data, and ML leaders to translate discovery workflows into scalable technical requirements and releases. Manage Agile delivery, technical risks, integrations, KPIs, external development partners, and architectural alignment across scientific platforms. Apply biology and laboratory workflow expertise to guide AI-enabled product development and technical trade-offs.

Own the technical product roadmap, backlog, architecture direction, and delivery cadence for Takeda’s AI-driven Discovery Automation and Robotics product family. Translate scientific and business goals into technical requirements, manage Agile delivery, guide engineering and data teams, oversee integrations with laboratory platforms, make technical trade-offs, manage risks and external partners, and ensure scalable solutions support automated small-molecule discovery workflows.

Leads the technical product strategy, backlog, architecture, and delivery roadmap for Takeda’s AI-native laboratory endpoint generation products. Translates scientific and business needs into technical requirements, coordinates engineering, data science, ML, and infrastructure teams, and ensures integration with laboratory platforms such as ELN/LIMS and scientific data lakes. Owns technical trade-offs, delivery quality, engineering KPIs, external partners, and alignment across the Labs of Tomorrow portfolio while enabling AI/ML-driven scientific workflow redesign.