Engagement Manager - Sales & Distribution

TrueBlue Inc.; Newark (NJ); On-site; USD 150,000 - 230,000; Full time

Job summary

TrueBlue Inc. is seeking an Engagement Manager (Sales & Distribution) in Newark, NJ to lead analytics and AI initiatives for asset management sales teams.

You will translate sales challenges into data-driven AI workflows, oversee data scientists, AI engineers and data engineers, and drive deployment from discovery through adoption. You will define analytics use cases, validate segmentation and scoring, and ensure data inputs from CRM, holdings, and third-party sources meet quality and licensing

Qualifications

• E1. 10–14 years of overall experience, including at least five years in sales and distribution analytics within asset management or directly supporting asset management clients.
• E2. Strong asset management sales and distribution knowledge applied to analytics and AI. Understand institutional investors, consultants, advisors, wholesalers and product specialists, and how mandates, allocations, flows and relationships inform analytical features and recommendations.
• E3. Practical experience analysing and combining CRM, relevant marketing data and external datasets for sales intelligence. Sources may include Preqin, eVestment, With Intelligence, Morningstar, consultant and mandate data, advisor/platform data and public research. Experience with every vendor is not required.
• E4. Demonstrated delivery of AI use cases used by sales or distribution teams, alongside strong analytical problem- solving skills. Explain personal ownership of data preparation, approach selection, evaluation, deployment and results. Exercise technical judgment on when to use rules, statistical methods, machine learning or generative AI.
• E5. Experience leading analytics, data science or AI delivery teams and senior stakeholders independently. Translate business needs into analytical and technical specifications, review solution quality, and take ownership from problem definition through implementation.
• E6. Experience defining evaluation measures, validating with users and driving adoption of analytics and AI solutions. Distinguish model or output quality, operational efficiency and commercial outcomes, and explain measurement limitations.

Responsibilities

• R1. Translate sales and distribution needs into analytics and AI use cases across prospecting, investor segmentation, consultant intelligence, account planning, pipeline analysis, RFP/DDQ support, cross-selling and retention. Define the decision, users, data and intended outcome for each solution.
• R2. Lead the design and validation of segmentation, prospect and opportunity scoring, whitespace analysis and sales performance analytics. Select suitable analytical approaches, define features and measures, and test the usefulness of results with sales teams.
• R3. Specify and assess data inputs for analytics and AI: CRM, holdings and exposure, sales activities, marketing engagement, third-party datasets and public signals. Evaluate coverage, grain, freshness, overlap, quality and licensing restrictions.
• R4. Own the analytical definitions and data requirements behind client and contact resolution, hierarchies, product mappings and reusable signals. Work with engineering teams to create reliable datasets and consistent measures for models, dashboards and AI workflows.
• R5. Own end-to-end delivery of AI solutions such as Client 360, prospect prioritization, consultant intelligence, meeting preparation, CRM assistants and next-best actions. Guide solution design, prototyping, evaluation, integration and deployment; define acceptance criteria and human review.
• R6. Independently lead client conversations about analytics and AI requirements and technical trade-offs. Direct multidisciplinary delivery teams, review analytical and AI outputs, resolve dependencies, and own client-ready solutions and deliverable quality.
• R7. Establish baselines and validate analytics and AI solutions with users. Embed outputs into sales workflows and track adoption, accuracy, insight quality, time saved, opportunity progression and business impact.
• R8. Establish evaluation and controls for AI outputs, including evidence grounding, unsupported-answer checks, permissions, traceability and ongoing monitoring. Partner with AI, CRM, platform, security and compliance teams to deliver reliable solutions.

Job description

Engagement Manager (Sales & Distribution)

Location: Newark, NJ

JOB DESCRIPTION

We are looking for an analytics and AI leader to design, build and deliver data-driven solutions for sales and distribution teams at asset management firms. The role owns analytical problem solving, AI use-case design and delivery, and the quality of solutions used for prospecting, relationship intelligence and sales productivity. The person will use deep asset management domain knowledge to translate sales challenges into analytical methods, data requirements and AI workflows. They will lead data scientists, AI engineers and data engineers from discovery through deployment and adoption, with the technical judgment to challenge approaches, evaluate outputs and resolve delivery issues.

Key Responsibilities:

• R1. Translate sales and distribution needs into analytics and AI use cases across prospecting, investor segmentation, consultant intelligence, account planning, pipeline analysis, RFP/DDQ support, cross-selling and retention. Define the decision, users, data and intended outcome for each solution.
• R2. Lead the design and validation of segmentation, prospect and opportunity scoring, whitespace analysis and sales performance analytics. Select suitable analytical approaches, define features and measures, and test the usefulness of results with sales teams.
• R3. Specify and assess data inputs for analytics and AI: CRM, holdings and exposure, sales activities, marketing engagement, third-party datasets and public signals. Evaluate coverage, grain, freshness, overlap, quality and licensing restrictions.
• R4. Own the analytical definitions and data requirements behind client and contact resolution, hierarchies, product mappings and reusable signals. Work with engineering teams to create reliable datasets and consistent measures for models, dashboards and AI workflows.
• R5. Own end-to-end delivery of AI solutions such as Client 360, prospect prioritization, consultant intelligence, meeting preparation, CRM assistants and next-best actions. Guide solution design, prototyping, evaluation, integration and deployment; define acceptance criteria and human review.
• R6. Independently lead client conversations about analytics and AI requirements and technical trade-offs. Direct multidisciplinary delivery teams, review analytical and AI outputs, resolve dependencies, and own client-ready solutions and deliverable quality.
• R7. Establish baselines and validate analytics and AI solutions with users. Embed outputs into sales workflows and track adoption, accuracy, insight quality, time saved, opportunity progression and business impact.
• R8. Establish evaluation and controls for AI outputs, including evidence grounding, unsupported-answer checks, permissions, traceability and ongoing monitoring. Partner with AI, CRM, platform, security and compliance teams to deliver reliable solutions.

Key Qualifications & Skills

• E1. 10–14 years of overall experience, including at least five years in sales and distribution analytics within asset management or directly supporting asset management clients.
• E2. Strong asset management sales and distribution knowledge applied to analytics and AI. Understand institutional investors, consultants, advisors, wholesalers and product specialists, and how mandates, allocations, flows and relationships inform analytical features and recommendations.
• E3. Practical experience analysing and combining CRM, relevant marketing data and external datasets for sales intelligence. Sources may include Preqin, eVestment, With Intelligence, Morningstar, consultant and mandate data, advisor/platform data and public research. Experience with every vendor is not required.
• E4. Demonstrated delivery of AI use cases used by sales or distribution teams, alongside strong analytical problem- solving skills. Explain personal ownership of data preparation, approach selection, evaluation, deployment and results. Exercise technical judgment on when to use rules, statistical methods, machine learning or generative AI.
• E5. Experience leading analytics, data science or AI delivery teams and senior stakeholders independently. Translate business needs into analytical and technical specifications, review solution quality, and take ownership from problem definition through implementation.
• E6. Experience defining evaluation measures, validating with users and driving adoption of analytics and AI solutions. Distinguish model or output quality, operational efficiency and commercial outcomes, and explain measurement limitations.