The MMM Solutions Engineer is a critical bridge role responsible for the successful implementation of Tinuiti's Bliss Point Data Scientific MMM Products. This role engages pre-SOW signing and stays closely involved through data onboarding and the client's first model readout — ensuring every engagement is technically sound, strategically set up, and delivered on time.
The ideal candidate has a strong technical background, the ability to interpret complex model outputs and translate them into actionable insights, and the communication skills to coach and enable client-facing teams. Strong technical project maclient retention (diana has specifically requested that we recruit externally, in addition to internal candidates)
nagement — coordinating stakeholders and holding teams to milestones — is essential, as is a comfort with process engineering and automation to make deployments more scalable over time.
- Product Scoping: Partner with Sales pre-SOW to assess product-client fit, define the right model scope (e.g., DTC vs. DTC and Commerce), and provide input on SOW language to ensure commitments are accurate and achievable.
- Technical Onboarding: Support end-to-end data onboarding for all MMM engagements, serving as the primary technical resource for client data questions while partnering with Client Reporting, Client Delivery, and Econometrics.
- Pre-Readout Enablement: Partner with Client Delivery to prepare for the first model readout — helping the team understand model outputs and coaching them on how to use our toolkit to frame budget recommendations and strategic decisions for the client.
- Client Data Science Engagement: Lead methodology walkthroughs for client data science teams, fielding technical questions about model construction, assumptions, and outputs.
- Cross-Functional Coordination: Serve as the central point of contact across Client Reporting, Econometrics, Sales, and Client Delivery — ensuring continuity, preventing over-promising, and holding stakeholders accountable to timelines.
- AI-Powered Efficiency & Process Optimization: Identify and implement opportunities to use AI tools, agents, and automation to make workflows faster and more scalable. Standardize implementation processes into practical, repeatable products.
- Product Feedback: Synthesize engagement patterns and client feedback to inform feature development and improve the overall offering.
- Mathematical Proficiency: Strong background in data analysis, statistics, or a related technical field — able to understand model methodologies and explain outputs to both technical and non-technical audiences.
- Project & Stakeholder Management: Proven ability to manage multiple concurrent engagements — building timelines, sequencing dependencies, and holding cross-functional partners accountable without direct authority.
- Data Literacy: Comfort in the "messy middle" of data and user needs, with an ability to simplify complex data sets into clear, actionable insights.
- Strategic Communication: Able to translate the "why" behind the "what" for internal stakeholders and external clients, including C-suite executives and client data science teams.
- Proactive Ownership: A "Maker's Mindset" — proposes solutions and drives projects forward with minimal hand-holding.
- Collaboration & Engineering Empathy: Experience partnering with model builders on technical hand-offs and QA.
- Client-Facing Experience: Comfortable with C-level executives and technical decision-makers; experience in a customer-facing or new-business-oriented role is a plus.
- Automation & AI Fluency: Demonstrated comfort using AI tools and automation to improve process efficiency, with a curiosity to keep pushing that capability forward.
The annual base salary range for this roles' listed level is currently $95,000-$105,000 plus performance bonus of up to 8%. Grade level and salary ranges are determined through interviews and a review of education, experience, knowledge, skills, abilities of the applicant, equity with other team members, and alignment with market data.
We will provide more information on our benefits and equity upon requests. Disclosure as required by the Colorado Equal Pay for > Equal Work Act, C.R.S. § 8-5-101 et seq