Andrew Furman

Andrew Furman is a strong match for this role, bringing 14+ years of experience in marketing technology, CRM workflow development, and AI automation consulting. He is actively building LLM-driven workflow automation using ChatGPT, Claude, Python, TypeScript, and APIs at his current role, directly aligning with PEG's core technical requirements. His 10-year PwC background includes hands-on Salesforce CRM administration, staffing operations, client onboarding, and executive stakeholder management—skills that transfer well to Bullhorn ATS optimization and working directly with PEG leadership. He lacks direct Bullhorn experience and staffing-industry-specific automation background, which are noted gaps but not disqualifying given his adjacent CRM depth. His availability (10–15 hrs/week remote) and consulting engagement model are a structural fit for this part-time freelance opportunity.

Andrew scores highly on AI automation tooling (ChatGPT, Claude, Python, APIs confirmed in resume and screening notes), CRM workflow development (Salesforce Apex, data imports, deduplication, segmentation), and consulting communication skills for non-technical teams. His current role at 8090 Solutions directly mirrors the execution-oriented automation build work PEG needs. Points are deducted for zero direct Bullhorn ATS experience, which is a named preferred skill and central to the role's CRM optimization workstream. He also has no confirmed LinkedIn automation or social selling infrastructure experience, another core responsibility area. Bonus skills like WordPress, SEO/AI optimization, and video workflow automation are not evidenced. The staffing industry experience is adjacent (PwC staffing plans and delivery teams) but not direct recruiting operations experience.

screen further

Andrew is a compelling candidate with strong AI automation and CRM credentials, but key gaps in Bullhorn ATS, Make.com/Zapier, and LinkedIn automation experience should be clarified before submission to ensure the client isn't surprised. A focused 20-minute follow-up call to probe those specific areas—and confirm no conflict-of-interest with his current role—will allow a confident, well-prepared submittal.

Decision support only. A recruiter reviews every recommendation.

• Actively building LLM-driven workflow automation with ChatGPT, Claude, Python, and APIs at a current AI-native company—directly mirrors PEG's core automation needs
• 10 years at PwC managing CRM-tracked consulting operations, staffing plans, client onboarding, and executive reporting—strong analog to working with PEG leadership
• Hands-on Salesforce CRM experience including Apex triggers, custom objects, data imports, deduplication, and segmentation—highly transferable to Bullhorn optimization work
• Proven ability to translate complex operational pain points into pragmatic automation roadmaps for non-technical stakeholders—critical for PEG's leadership-facing engagement model
• Carnegie Mellon Information Systems background plus Accenture enterprise software development experience adds technical credibility
• Availability (10–15 hrs/week, remote) and freelance structure align precisely with the engagement model
• Screening notes confirm hands-on, energetic, execution-oriented personality consistent with PEG's stated ideal consultant profile

• No direct Bullhorn ATS experience documented anywhere in resume or screening notes—Bullhorn optimization is a named core responsibility and may require a ramp period
• No evidence of LinkedIn automation tools or social selling infrastructure work, which is a significant portion of the role's scope
• No confirmed Make.com or Zapier experience; these are listed as preferred tools and are central to the automation stack
• SEO and AI discoverability work is listed as a responsibility but is not evidenced in the resume
• WordPress familiarity is listed as a bonus skill with no evidence in the resume
• Current full-time role at 8090 Solutions may create bandwidth constraints or conflict-of-interest considerations if PEG clients overlap with enterprise sectors he serves there
• All CRM experience is Salesforce-based; Bullhorn has a meaningfully different data model and workflow paradigm that may require onboarding investment

• Has candidate used Make.com or Zapier specifically, or does he rely primarily on custom Python/API builds for automation?
• Any prior exposure to Bullhorn ATS, even as an end user or integration partner?
• Has candidate done LinkedIn automation or social selling infrastructure work (e.g., Expandi, Dux-Soup, Phantombuster, or native LinkedIn tools)?
• Does current role at 8090 Solutions have any non-compete or client conflict provisions that could affect work with a staffing firm?
• What is the candidate's hourly rate expectation, and has it been formally confirmed against budget?
• Does candidate have any SEO, AI search visibility, or content marketing operations experience?
• Is candidate open to a short paid discovery or pilot project to validate Bullhorn ramp speed before a longer engagement?

Andrew Furman is a marketing technology and AI automation consultant with 14+ years of experience building operational software, CRM workflows, and AI-driven automation systems for large enterprise clients. He is currently serving as a Member of Technical Staff at an AI-native software company where he builds and deploys LLM-driven workflow automation using ChatGPT, Claude, Python, TypeScript, and APIs—translating complex operational needs into working tools and integrations for CIO and COO-level buyers. Prior to this, Andrew spent 10 years at PwC as a Client Consulting Director, where he managed staffing operations, client onboarding, engagement financials, and CRM-tracked delivery programs through Salesforce, and earlier built Salesforce CRM customizations hands-on at Accenture. He is available 10–15 hours per week on a fully remote, part-time consulting basis, communicates clearly with non-technical teams, and brings a practical, execution-first mindset that aligns well with PEG's goal of building scalable automation infrastructure quickly and efficiently.

• Walk us through a specific automation you've built recently using ChatGPT or Claude—what was the manual process before, what did you build, and how did you deploy it for a non-technical team?
• You haven't used Bullhorn directly, but you have deep Salesforce CRM experience. How would you approach learning Bullhorn's data model and workflow engine quickly enough to start adding value in the first 30 days?
• Have you worked with Make.com or Zapier, or do you typically build integrations through custom Python/API code? How do you decide which approach to use for a given automation problem?
• PEG wants to build a scalable LinkedIn social selling system for its recruiters. What tools, workflows, or automation approaches would you explore first, and what would your discovery process look like?
• Describe a situation where you had to simplify a complex technical system or automation for a non-technical leadership team. How did you communicate it, and how did you ensure adoption?
• We have a large Bullhorn database with duplicate records and inconsistent data. What's your process for scoping and executing a data cleanup and deduplication project without disrupting active recruiting workflows?
• This is a part-time engagement and you're currently in a full-time role. How do you manage competing priorities, and are there any conflict-of-interest considerations we should discuss upfront?