Strategy & Solutions Lead

Causaly•New York, NY

2d•$180,000 - $240,000

About The Position

Causaly is hiring a Strategy and Solutions Lead to incubate and validate new growth opportunities at the intersection of AI, data, product, and pharma and biotech customers across R&D and commercial functions. The role focuses on new capabilities, solution lines, integrations, and market segments where customer demand exceeds what the product supports out of the box. The Lead turns ambiguous enterprise demand into scalable, repeatable solution plays grounded in pharma workflows, drives executive decision-making, and hands validated plays into business-as-usual teams. The role works directly with Causaly’s executive leadership and senior customer stakeholders across pharma R&D, commercial, data, and technology teams. It owns opportunities from initial strategic conversation through executive decision and handoff, with significant autonomy. The position also develops technical solution designs, validates feasibility, builds repeatable playbooks, supports a small number of lighthouse opportunities, and ensures validated solutions can be transferred to ongoing teams.

Requirements

At least 7 years of experience at the intersection of pharma or life sciences strategy, AI and data, and enterprise customers.
A track record of leading discussions with senior R&D and commercial stakeholders and shaping ambiguous requests into scalable solutions.
Background in life science strategy or AI consulting at Engagement Manager or Associate Partner level, with substantial R&D or commercial work and ownership of AI or data programmes from strategy through scale-up; or experience in Enterprise Solutions or Solutions Strategy for B2B SaaS serving pharma and biotech, particularly AI-, data-, or platform-heavy products.
Deep understanding of end-to-end pharma R&D and commercial workflows and their decisions, including target identification, Target Product Profile development, candidate selection, indication expansion, launch, and lifecycle planning, and of how data and AI can affect them.
Hands-on experience building, deploying, and/or integrating technology solutions in enterprise settings, such as data platforms, AI and LLM applications, APIs, and system integrations, including seeing solutions through to production.
Ability to read and challenge solution architectures, reason through data pipelines, APIs, and integration patterns, and understand how AI and LLM systems are built and evaluated, including retrieval-augmented generation, agentic workflows, and knowledge graphs.
Experience turning complex enterprise demand into repeatable playbooks, including qualification criteria, scoping frameworks, pilot design, and business-as-usual handoff.
Strong judgement in making and defending pursue, park, or decline recommendations based on product direction, feasibility, scalability, and business impact.
Ability to drive executive decision-making internally and with customers across Product, Engineering, and GTM, and translate technical trade-offs into clear business choices.

Nice To Haves

Scientific or quantitative background, such as an advanced degree in life sciences, engineering, or a related field.
Experience on AI or GenAI programmes in pharma, from use-case definition through scaled deployment.
Prior technical experience, such as as a forward deployed engineer, data scientist, software engineer, or technical product manager.
Familiarity with enterprise data governance in regulated environments, including GxP, data privacy, and information security reviews.
Exposure to market expansion, new segment go-to-market, technology partnerships, or solution incubation.

Responsibilities

Own intake and shaping of strategic enterprise opportunities requiring new capabilities, integrations, packaging, or market positioning.
Drive executive decisions to pursue, park, or decline opportunities, presenting rationale, options, and business and technical implications.
Maintain a clear boundary between incubation work and business-as-usual work.
Lead senior discussions across pharma R&D and commercial functions, grounding discovery in end-to-end workflows and decision points.
Translate ambiguous customer requests into solution concepts and delivery paths, including value, scope, sequencing, timelines, risks, dependencies, and mutual commitments.
Set scalable expectations and commercial framing, distinguishing standard, configurable, investment-dependent, and out-of-scope work.
Work with customer architecture, data, IT, and AI teams on platform capabilities, APIs, data models, and integration patterns.
Drive solution design for new plays, including integration approach, data flows, security, and deployment model, and work with Engineering and Data through build, testing, and deployment.
Assess technical feasibility, review architectures, identify integration and data risks, and determine what can be configured or must be built and the effort involved.
Shape and evaluate technology partnerships, including strategic rationale, technical fit, integration approach, and joint solution design.
Represent Causaly’s platform and roadmap in executive and technical forums, explaining current capabilities, configurability, and future direction.
Develop repeatable solution playbooks, including offering definitions, use cases, packaging, qualification criteria, scoping templates, success plans, delivery blueprints, reference architectures, and enablement assets.
Standardise complex offerings such as Private Data, enterprise integrations, and AI workflows on customer data into productised motions with clear patterns, governance, and pilot-to-rollout pathways.
Partner with Product, Engineering, and Data to define feasibility options and reference architectures and make product and investment recommendations.
Co-sponsor a small number of lighthouse opportunities with Sales to validate new solution plays.
Coordinate early cross-functional work, including pilot design, governance, and decision-making, with the goal of making plays transferable and repeatable.
Hand validated plays into Sales, Customer Success, Delivery, and Product, providing training, assets, decision trees, and clear ownership.
Maintain a structured feedback loop into Product on product candidates, investment needs, and constraints learned from enterprise demand.